- The report tracks daily ETF data, focusing on "broad-based" and "thematic" ETFs, categorized by the indices they track, such as CSI 300, CSI 500, and industry/style indices like non-bank financials and dividends[3] - The RSI (Relative Strength Index) is calculated using the formula: $ RSI = 100 - 100 / (1 + RS) $ where RS represents the ratio of average gains to average losses over a 12-day period. RSI > 70 indicates an overbought market, while RSI < 30 indicates an oversold market[3] - 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) is the ETF's net asset value on day T, and R(T) is the return on day T[3] - Intraday price trends are derived from 5-minute interval transaction prices, with red dots marking the highest and lowest prices of the day. Some intraday data may be missing due to data limitations[3] - Institutional holdings are estimated based on the latest annual or semi-annual reports, excluding holdings by linked funds. These values are subject to estimation errors[4]

- The report introduces the **RSI (Relative Strength Index)** as a quantitative factor. The construction idea is to measure the market's overbought or oversold conditions based on price movements over a specific period. The formula is: $ RSI = 100 - 100 / (1 + RS) $ where RS represents the ratio of average gains to average losses over a 12-day period. RSI > 70 indicates an overbought market, while RSI < 30 indicates an oversold market[2][4] - Another quantitative factor mentioned is **Net Purchase Amount (NETBUY)**, which evaluates the net inflow or outflow of funds into 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 rate 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 data excludes holdings by linked funds and is subject to estimation errors[3] - The report provides a detailed breakdown of ETF performance across various indices, including **broad-based indices** like CSI 300, CSI 500, and CSI A500, as well as **thematic indices** such as non-bank financials, dividends, and sector-specific indices like semiconductors and renewable energy[4][7] - The report highlights the **T+0 trading mechanism**, indicating whether ETFs support same-day buy-and-sell transactions. This feature is noted for certain ETFs, particularly those tracking overseas indices like Hong Kong, U.S., and Japan markets[2][4] - The report includes **daily intraday price trends**, constructed using 5-minute interval transaction prices. Red dots mark the highest and lowest prices of the day, though some data gaps may exist due to missing intraday data[2] - The report categorizes ETFs into **broad-based** and **thematic** groups, analyzing their tracking indices, management fees, institutional holdings, and other metrics. Examples include CSI 300 ETFs, CSI 500 ETFs, and thematic ETFs like semiconductors, renewable energy, and artificial intelligence[4][7]

1. Report Industry Investment Rating No relevant content provided. 2. Core Viewpoints of the Report - The report analyzes the secondary - market situation, ETF product profiles (including market performance, fund flows, trading volumes, margin trading, and new issuance/listing) of ETF funds from May 26, 2025, to May 30, 2025 [1][21]. 3. Summary by Relevant Catalogs 3.1 Secondary Market Overview - In the sample period, Nikkei 225, S&P 500, and CSI 2000 had the top weekly returns, at 2.17%, 1.88%, and 1.09% respectively. The PE valuation quantile of STAR 50 was the highest at 92.98%, while that of S&P 500 was the lowest at 26.80% [10]. - Among Shenwan primary industries, environmental protection, pharmaceutical biology, and national defense and military industry had the top returns, at 3.42%, 2.21%, and 2.13% respectively. Automobile, power equipment, and non - ferrous metals had relatively low returns, at - 4.11%, - 2.44%, and - 2.40% respectively. The industries with the highest valuation quantiles were environmental protection, pharmaceutical biology, and banks, at 99.59%, 98.76%, and 98.35% respectively. The industries with lower valuation quantiles were comprehensive, non - bank finance, and non - ferrous metals, at 2.89%, 4.55%, and 5.79% respectively [16]. 3.2 ETF Product Profiles 3.2.1 ETF Market Performance - Based on different classifications, style ETFs had the best average performance with a weighted average return of 0.32%, while commodity ETFs had the worst with a weighted average return of - 1.09%. - In terms of the listing plate, ETFs corresponding to Japanese stocks and CSI 2000 performed well, with weighted average returns of 1.72% and 1.37% respectively. MSCI China A - share concept and GEM - related ETFs performed poorly, with weighted average returns of - 1.83% and - 1.24% respectively [21]. 3.2.2 ETF Fund Flows - From the perspective of different categories, bond ETFs had the largest net inflow of 15.44 billion yuan, while industry - themed ETFs had the smallest net inflow of - 1.441 billion yuan. - From the perspective of the listing plate, GEM - related ETFs had the largest net inflow of 1.67 billion yuan, while Hong Kong stock ETFs had the smallest net inflow of - 3.532 billion yuan. - From the industry sector perspective, technology sector ETFs had the largest net inflow of 2.819 billion yuan, while biomedical sector ETFs had the smallest net inflow of - 1.744 billion yuan. - From the theme perspective, chip semiconductor and dividend ETFs had the largest net inflows of 1.45 billion yuan and 0.995 billion yuan respectively. Central state - owned enterprises and innovative drug ETFs had the smallest net inflows of - 0.518 billion yuan and - 0.491 billion yuan respectively [2][25][27]. 3.2.3 ETF Trading Volume - From the perspective of different categories, the daily average trading volume change rate of money market ETFs increased the most, at 10.43%, while that of commodity ETFs decreased the most, at - 34.99%. - From the perspective of the listing plate, the daily average trading volume change rate of Japanese stock ETFs increased the most, at 13.80%, while that of GEM - related ETFs decreased the most, at - 21.97%. - From the industry sector perspective, the daily average trading volume change rate of biomedical sector ETFs increased the most, at 7.99%, while that of the financial real - estate sector decreased the most, at - 29.50%. - From the theme perspective, chip semiconductor and dividend ETFs had the largest 5 - day average daily trading volumes of 2.942 billion yuan and 2.055 billion yuan respectively. Innovative drug and artificial intelligence ETFs had the largest increase or the smallest decrease in the daily average trading volume change rate, at 12.32% and - 9.06% respectively. Military and non - bank ETFs had the largest decrease or the smallest increase in the daily average trading volume change rate, at - 40.90% and - 35.56% respectively [32][35][38][40]. 3.2.4 ETF Margin Trading - In the sample period, the net margin purchase of all equity ETFs was 177 million yuan, and the net short - selling was - 35 million yuan. GF CSI A500ETF had the largest net margin purchase, and Huaxia CSI 1000ETF had the largest net short - selling [2][45]. 3.2.5 ETF New Issuance and Listing - During the sample period, 6 funds were established and 9 funds were listed [3][47].

Group 1: Manufacturing PMI Insights - In May 2025, the Manufacturing PMI improved to 49.5%, showing a month-on-month increase and remaining stable compared to May 2024[8] - The production index rose by 0.9 percentage points to 50.7%, indicating a return to the expansion zone[8] - The new orders index increased by 0.6 percentage points to 49.8%, while the new export orders index rose by 2.8 percentage points to 47.5%[8] - The ex-factory price index has decreased for three consecutive months, recording 44.7% in May, indicating ongoing profit pressure for enterprises[8] Group 2: Non-Manufacturing PMI Trends - The Non-Manufacturing PMI stood at 50.3% in May, a slight decrease of 0.1 percentage points from the previous month, but still indicating expansion[13] - The new orders index for non-manufacturing recorded 46.1%, up by 1.2 percentage points from last month[13] - The service sector PMI slightly increased to 50.2%, driven by improvements in the life service industry, particularly during the "May Day" holiday[17] Group 3: Sector Performance and Economic Activity - The construction PMI decreased by 0.9 percentage points to 51.0%, with new orders and employment indices showing declines[17] - High-frequency data indicated that the operating rates for automotive tires and blast furnaces improved, with rates of 73.1% and 84.1% respectively[20] - The overall market activity in sectors like transportation and hospitality saw significant increases due to holiday effects, enhancing business activity indices[17]

Report Industry Investment Rating - Not provided in the content Core Viewpoints - The report analyzes the secondary market and ETF product situation from multiple perspectives, including index trends, ETF market performance, fund flows, trading volume, margin trading, and new fund launches during the sample period from May 19 to May 23, 2025 [1][10] Summary by Directory 1. Secondary Market Overview - SGE Gold 9999, Hang Seng Index, and CSI 300 had the top weekly returns, at 3.69%, 1.10%, and -0.18% respectively. The Hang Seng Index had the highest PE valuation quantile at 95.08%, while the Nikkei 225 had the lowest at 13.11% [10] - In the Shenwan primary industries, Medicine and Biology, Comprehensive, and Non - Ferrous Metals had the top returns at 1.78%, 1.41%, and 1.26% respectively. Computer, Machinery, and Communication had the lowest returns at -3.02%, -2.48%, and -2.31% respectively. The industries with the highest valuation quantiles were Banking, Textile and Apparel, and Beauty Care, while those with the lowest were Agriculture, Forestry, Animal Husbandry and Fishery, Non - Banking Finance, and Comprehensive [14] 2. ETF Product Overview 2.1 ETF Market Performance - Commodity ETFs had the best average performance with a weighted average return of 3.74%, while QDII ETFs had the worst at -0.64% [18] - Among ETFs classified by listing sectors, those related to Hong Kong stocks and MSCI China A - share concepts performed well with weighted average returns of 0.39% and 0.29% respectively, while those related to US stocks and the Science and Technology Innovation Board performed poorly at -1.40% and -1.29% respectively [18] - Among industry - themed ETFs, Biomedical ETFs had the best performance with a weighted average return of 2.13%, while Technology ETFs had the worst at -1.82% [20] - From a theme perspective, Innovative Drug and Dividend ETFs performed well with weighted average returns of 4.54% and 0.72% respectively, while Robot and Non - Banking ETFs performed poorly at -2.89% and -2.11% respectively [20] 2.2 ETF Fund Inflows and Outflows - From the perspective of different types of ETFs, Bond ETFs had the largest net inflow of 11.356 billion yuan, while Broad - based ETFs had the smallest at -14.283 billion yuan [22] - From the perspective of ETF tracking indices and listing sectors, Science and Technology Innovation Board - related ETFs had the largest net inflow of 1.389 billion yuan, while CSI 300 ETFs had the smallest at -3.459 billion yuan [22] - From the industry sector perspective, Traditional Manufacturing sector ETFs had the largest net inflow of 2.566 billion yuan, while Biomedical sector ETFs had the smallest at -3.4 billion yuan [25] - From the theme perspective, Military and Chip Semiconductor ETFs had the largest net inflows of 2.532 billion yuan and 1.167 billion yuan respectively, while Innovative Drug and Dividend ETFs had the smallest at -1.481 billion yuan and -0.589 billion yuan respectively [25] 2.3 ETF Trading Volume - From the perspective of different types of ETFs, Bond ETFs had the largest increase in the average daily trading volume change rate at 9.57%, while Commodity ETFs had the largest decrease at -29.12% [31] - From the perspective of ETF tracking indices and listing sectors, US stock ETFs had the largest increase in the average daily trading volume change rate at 5.98%, while Japanese stock ETFs had the largest decrease at -35.41% [34] - From the industry sector perspective, Biomedical sector ETFs had the largest increase in the average daily trading volume change rate at 16.49%, while Technology sector ETFs had the largest decrease at -19.87% [35] - From the theme perspective, Chip Semiconductor and Dividend ETFs had the largest average daily trading volumes in the past 5 days, at 2.73 billion yuan and 2.117 billion yuan respectively. Innovative Drug and Banking ETFs had the largest increases or smallest decreases in the average daily trading volume change rate at 19.93% and 14.86% respectively, while Consumer Electronics and Robot ETFs had the largest decreases or smallest increases at -34.16% and -29.14% respectively [39] 2.4 ETF Margin Trading - During the sample period, the net margin purchase of all equity ETFs was -362 million yuan, and the net short - selling was 282 million yuan. Cathay Pacific CSI All - Index Securities Company ETF had the largest net margin purchase, and Southern CSI 500 ETF had the largest net short - selling [2][45] 2.5 ETF New Launches and Listings - During the sample period, 13 funds were established and 3 funds were listed [3][47]

Investment Rating - The industry investment rating is "Outperform" [1][52]. Core Insights - The report emphasizes the importance of solid-state batteries at this time due to their potential for high safety, energy density, and temperature adaptability [25][45]. - The solid-state battery sector is currently at a valuation low point, with a price-to-earnings ratio of 30x, which is approximately 65% of its previous peak [13][22]. - There is an expectation for valuation recovery in the solid-state battery sector due to recent product launches and industry discussions [22][5]. Summary by Sections Valuation - The solid-state battery sector is currently valued at 30x, with a potential excess return of up to 25.7% compared to lithium battery stocks [13][5]. - Recent product launches and industry conferences are expected to catalyze an increase in sector valuations [22][5]. Pain Points - The main challenges for solid-state batteries include the ionic conductivity of electrolytes, with only sulfide electrolytes currently meeting liquid standards [3][28]. - Various solid-state electrolytes face issues such as low ionic conductivity and stability, with research focusing on improving these properties through doping and pressure application [27][28]. Key Segments - **Electrolytes**: The report highlights the potential of sulfide electrolytes, which are closest to liquid electrolytes in terms of ionic conductivity. Companies like Xiamen Tungsten and Yuyuan New Materials are noted for their advancements [45][3]. - **Equipment**: The report identifies dry electrode technology as a promising area, with companies like Naconor and Manstein leading in this space. The dry process eliminates solvents, reducing production costs and enhancing efficiency [46][3].

Report Industry Investment Rating - No relevant content provided Core Viewpoints - The report analyzes the secondary market situation, ETF product situation (including market performance, fund inflow and outflow, trading volume, margin trading, and new issuance and listing) of ETF funds from May 12 to May 16, 2025 [1][10][18] Summary by Directory 1 Secondary Market Overview - In the sample period, the S&P 500, Hang Seng Index, and ChiNext Index ranked among the top in weekly returns, at 5.27%, 2.09%, and 1.38% respectively. The PE valuation quantiles of CSI 2000 and STAR 50 were the highest at 96.28%, while that of the Nikkei 225 was the lowest at 22.95% [10] - In the sample period, the beauty care, non - bank finance, and automobile industries ranked among the top in returns, at 3.08%, 2.49%, and 2.40% respectively. The computer, national defense and military industry, and media industries ranked relatively low, at - 1.26%, - 1.18%, and - 0.77% respectively. The industries with the highest valuation quantiles were textile and apparel, beauty care, and banking, at 100.00%, 99.59%, and 98.76% respectively. The industries with lower valuation quantiles were agriculture, forestry, animal husbandry and fishery, comprehensive, and non - ferrous metals, at 1.65%, 3.31%, and 7.23% respectively [14] 2 ETF Product Overview 2.1 ETF Market Performance - QDII ETFs had the best average performance with a weighted average return of 2.53%, while commodity ETFs had the worst average performance with a weighted average return of - 4.58% [18] - ETFs corresponding to US stocks and MSCI China A - share concepts had better market performance, with weighted average returns of 4.73% and 1.43% respectively. STAR - related and CSI 1000 ETFs had worse market performance, with weighted average returns of - 1.25% and - 0.27% respectively [18] - Financial real - estate sector ETFs had the best average performance with a weighted average return of 1.78%, while technology sector ETFs had the worst average performance with a weighted average return of - 1.48% [20] - Non - bank and low - carbon environmental protection ETFs had better performance, with weighted average returns of 2.08% and 1.72% respectively. Chip semiconductor and artificial intelligence ETFs had relatively poor average performance, with weighted average returns of - 1.95% and - 0.90% respectively [20] 2.2 ETF Fund Inflow and Outflow - From the perspective of different types of ETFs, bond ETFs had the largest net fund inflow of 9.342 billion yuan, while broad - based ETFs had the smallest net fund inflow of - 19.259 billion yuan [2][23] - From the perspective of ETF tracking indexes and the listing sectors of their constituent stocks, STAR - related ETFs had the largest net fund inflow of 2.456 billion yuan, while Hong Kong stock ETFs had the smallest net fund inflow of - 7.408 billion yuan [2][23] - From the perspective of industry sectors, technology sector ETFs had the largest net fund inflow of 1.853 billion yuan, while biomedical sector ETFs had the smallest net fund inflow of - 1.521 billion yuan [2][26] - From the perspective of themes, chip semiconductor and military - industry ETFs had the largest net fund inflows of 2.349 billion yuan and 0.248 billion yuan respectively. Dividend and non - bank ETFs had the smallest net fund inflows of - 3.685 billion yuan and - 1.142 billion yuan respectively [2][26] 2.3 ETF Trading Volume - From the perspective of different types of ETFs, QDII ETFs had the largest increase in the average daily trading volume change rate of 18.07%, while commodity ETFs had the largest decrease in the average daily trading volume change rate of - 15.72% [32] - From the perspective of ETF tracking indexes and the listing sectors of their constituent stocks, US stock ETFs had the largest increase in the average daily trading volume change rate of 42.38%, while CSI 300 had the largest decrease in the average daily trading volume change rate of - 12.21% [35] - From the perspective of industry sectors, the financial real - estate sector had the largest increase in the average daily trading volume change rate of 40.46%, while the consumption sector had the largest decrease in the average daily trading volume change rate of - 7.52% [38] - From the perspective of themes, non - bank and chip semiconductor ETFs had the largest average daily trading volumes in the past 5 days, at 3.822 billion yuan and 3.524 billion yuan respectively. Military - industry and non - bank ETFs had the largest increase or the smallest decrease in the average daily trading volume change rate, at 60.37% and 53.07% respectively. Innovative drug and chip semiconductor ETFs had the largest decrease or the smallest increase in the average daily trading volume change rate, at - 7.65% and - 3.62% respectively [39] 2.4 ETF Margin Trading - In the sample period, the net margin purchase of all equity ETFs was - 512 million yuan, and the net short - selling was - 65 million yuan. Huaxia Shanghai Stock Exchange STAR Market 50 ETF had the largest net margin purchase, and Huaxia CSI 1000 ETF had the largest net short - selling [2][44] 2.5 ETF New Issuance and Listing - In the sample period, a total of 3 funds were established and 13 funds were listed [3][46]

- Model Name: RSI (Relative Strength Index); Model Construction Idea: RSI is used to measure the speed and change of price movements, indicating overbought or oversold conditions; Model Construction Process: The formula for RSI is $ 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. RSI > 70 indicates overbought conditions, and RSI < 30 indicates oversold conditions[2][4] - Model Name: NETBUY; Model Construction Idea: NETBUY measures the net purchase amount of ETFs; Model Construction Process: The formula for NETBUY is $ NETBUY(T) = NAV(T) - NAV(T-1) * (1 + R(T)) $, where NETBUY(T) is the net purchase 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 trading day[2] - RSI Model, RSI Value: 60.23 for Huatai-PineBridge CSI 300 ETF[4] - RSI Model, RSI Value: 58.10 for E Fund CSI 300 ETF[4] - RSI Model, RSI Value: 59.67 for ChinaAMC CSI 300 ETF[4] - RSI Model, RSI Value: 58.70 for Harvest CSI 300 ETF[4] - RSI Model, RSI Value: 60.76 for Tianhong CSI 300 ETF[4] - RSI Model, RSI Value: 50.45 for Southern CSI 500 ETF[4] - RSI Model, RSI Value: 50.08 for ChinaAMC CSI 500 ETF[4] - RSI Model, RSI Value: 50.46 for Harvest CSI 500 ETF[4] - RSI Model, RSI Value: 50.20 for E Fund CSI 500 ETF[4] - RSI Model, RSI Value: 60.69 for ChinaAMC SSE 50 ETF[4] - RSI Model, RSI Value: 65.81 for E Fund SSE 50 ETF[4] - RSI Model, RSI Value: 59.60 for CCB SSE 50 ETF[4] - RSI Model, RSI Value: 56.69 for ChinaAMC CSI 800 ETF[4] - RSI Model, RSI Value: 57.98 for E Fund CSI 800 ETF[4] - RSI Model, RSI Value: 54.42 for Penghua CSI 800 ETF[4] - RSI Model, RSI Value: 51.74 for Southern CSI 1000 ETF[4] - RSI Model, RSI Value: 51.19 for ChinaAMC CSI 1000 ETF[4] - RSI Model, RSI Value: 51.25 for GF CSI 1000 ETF[4] - RSI Model, RSI Value: 57.14 for Huatai-PineBridge CSI 2000 ETF[4] - RSI Model, RSI Value: 58.35 for Southern CSI 2000 ETF[4] - RSI Model, RSI Value: 59.87 for ChinaAMC CSI 2000 ETF[4] - RSI Model, RSI Value: 65.42 for Ping An CSI A50 ETF[4] - RSI Model, RSI Value: 64.25 for Dacheng CSI A50 ETF[4] - RSI Model, RSI Value: 63.79 for E Fund CSI A50 ETF[4] - RSI Model, RSI Value: 60.22 for Huabao CSI A100 ETF[4] - RSI Model, RSI Value: 62.72 for GF CSI A100 ETF[4] - RSI Model, RSI Value: 58.55 for Guotai CSI A500 ETF[4] - RSI Model, RSI Value: 58.36 for Southern CSI A500 ETF[4] - RSI Model, RSI Value: 57.83 for GF CSI A500 ETF[4] - RSI Model, RSI Value: 41.09 for ChinaAMC SSE STAR 50 ETF[4] - RSI Model, RSI Value: 40.51 for E Fund SSE STAR 50 ETF[4] - RSI Model, RSI Value: 39.50 for ICBC SSE STAR 50 ETF[4] - RSI Model, RSI Value: 45.71 for Bosera SSE STAR 100 ETF[4] - RSI Model, RSI Value: 45.01 for Penghua SSE STAR 100 ETF[4] - RSI Model, RSI Value: 45.73 for ChinaAMC SSE STAR 100 ETF[4] - RSI Model, RSI Value: 56.19 for E Fund GEM ETF[4] - RSI Model, RSI Value: 56.78 for Tianhong GEM ETF[4] - RSI Model, RSI Value: 56.66 for GF GEM ETF[4] - RSI Model, RSI Value: 52.54 for ChinaAMC Hang Seng Tech ETF[4] - RSI Model, RSI Value: 61.83 for ChinaAMC Hang Seng ETF[4] - RSI Model, RSI Value: 62.68 for ChinaAMC HK Connect Hang Seng ETF[4] - RSI Model, RSI Value: 63.26 for Huaan Mitsubishi UFJ Nikkei 225 ETF[4] - RSI Model, RSI Value: 60.82 for ChinaAMC Nomura Nikkei 225 ETF[4] - RSI Model, RSI Value: 70.66 for GF NASDAQ 100 ETF[4] - RSI Model, RSI Value: 71.75 for Guotai NASDAQ 100 ETF[4] - RSI Model, RSI Value: 67.54 for Bosera S&P 500 ETF[4] - RSI Model, RSI Value: 61.20 for Penghua DJIA ETF[4] - RSI Model, RSI Value: 51.62 for Huatai-PineBridge South-East Asia ETF[4] - RSI Model, RSI Value: 61.44 for Huaan Germany (DAX) ETF[4] - RSI Model, RSI Value: 64.47 for Huabao CSI Bank ETF[4] - RSI Model, RSI Value: 64.89 for Tianhong CSI Bank ETF[4] - RSI Model, RSI Value: 64.85 for Southern CSI Bank ETF[4] - RSI Model, RSI Value: 64.64 for E Fund CSI Bank ETF[4] - RSI Model, RSI Value: 58.16 for Huatai-PineBridge Dividend ETF[4] - RSI Model, RSI Value: 59.96 for Huatai-PineBridge Low Volatility Dividend ETF[4] - RSI Model, RSI Value: 58.13 for Invesco Great Wall Low Volatility Dividend 100 ETF[4] - RSI Model, RSI Value: 60.65 for E Fund Dividend ETF[4] - RSI Model, RSI Value: 60.37 for CMB Dividend ETF[4] - RSI Model, RSI Value: 59.59 for Tianhong Low Volatility Dividend 100 ETF[4] - RSI Model, RSI Value: 66.12 for GF CSI Hong Kong Dividend ETF[4] - RSI Model, RSI Value: 65.18 for Invesco Great Wall CSI Hong Kong Dividend ETF[4] - RSI Model, RSI Value: 63.84 for Southern CSI Hong Kong Dividend ETF[4] - RSI Model, RSI Value: 59.70 for ChinaAMC New Energy ETF[4] - RSI Model, RSI Value: 56.63 for Southern New Energy ETF[4] - RSI Model, RSI Value: 60.42 for Ping An New Energy Industry ETF[4] - RSI Model, RSI Value: 62.27 for GF New Energy Vehicle Battery ETF[4] - RSI Model, RSI Value: 50.49 for Penghua SSE STAR New Energy ETF[4] - RSI Model, RSI Value: 38.96 for Huatai-PineBridge Semiconductor ETF[4] - RSI Model, RSI Value: 40.19 for Guolian CSI Semiconductor ETF[4] - RSI Model, RSI Value: 39.65 for Guotai CES Semiconductor ETF[4] - RSI Model, RSI Value: 39.38 for Harvest SSE STAR Semiconductor ETF[4] - RSI Model, RSI Value: 39.29 for Huaan SSE STAR Semiconductor ETF[4] - RSI Model, RSI Value: 50.55 for Huatai-PineBridge Photovoltaic Industry ETF[4] - RSI Model, RSI Value: 51.07 for Tianhong Photovoltaic Industry ETF[4] - RSI Model, RSI Value: 51.54 for Guotai Military ETF[4] - RSI Model, RSI Value: 51.94 for GF Military Leaders ETF[4] - RSI Model, RSI Value: 51.54 for GF Military ETF[4] - RSI Model, RSI Value: 61.07 for ChinaAMC Low Carbon Economy ETF[4] - RSI Model, RSI Value: 62.60 for E Fund Shanghai Carbon Neutrality ETF[4] - RSI Model, RSI Value: 57.16 for GF Environmental Protection Industry ETF[4] - RSI Model, RSI Value: 53.41 for ChinaAMC Robotics ETF[4] - RSI Model, RSI Value: 53.16 for Tianhong Robotics ETF[4] - RSI Model, RSI Value: 53.36 for Guotai Robotics ETF[4] - RSI Model, RSI Value: 50.82 for Harvest Central Enterprise Innovation ETF[4] - RSI Model, RSI Value: 50.12 for Huafu Artificial Intelligence Industry ETF[4] - RSI Model, RSI Value: 42.00 for Southern Real Estate ETF[4] - RSI Model, RSI Value: 37.08 for Huabao

Market Liquidity Overview - R007 decreased from 1.8396% to 1.5805%, a reduction of 25.91 basis points; DR007 fell from 1.7986% to 1.5409%, down 25.77 basis points [16] - The net inflow of funds this week was -14.82 billion yuan, an increase of 17.436 billion yuan compared to last week, with total fund supply at 31.553 billion yuan and demand at 46.373 billion yuan [21] Industry Sector Liquidity Tracking - All industry sectors showed an upward trend this week, with significant increases in defense and military (up 6.44%) and telecommunications (up 5.43%); consumer services had the smallest increase at 0.30% [29] - The electronic industry had the highest net buy of leveraged funds at 4.157 billion yuan, while the banking sector had the highest net sell at 2.158 billion yuan [31] - The power equipment and renewable energy sector saw the most net inflow of main funds at 1.939 billion yuan, while the electronic sector experienced the largest net outflow at 2.212 billion yuan [35] Style Sector Liquidity Tracking - All style indices experienced increases, with the financial style showing the most significant rise; the average daily trading volume of the cyclical style was at a high compared to the past month, three months, and six months [5]

Quantitative Models and Construction Methods Model Name: RSI (Relative Strength Index) - Model Construction Idea: The RSI is used to measure the speed and change of price movements, indicating whether a market is overbought or oversold[2] - Model Construction Process: - Calculate the average gain and average loss over a specified period (12 days) - Use the formula: $ RSI = 100 - 100 / (1 + RS) $, where RS is the ratio of average gain to average loss[2] - Model Evaluation: The RSI is a widely used momentum indicator that helps identify potential reversal points in the market[2] Model Name: NETBUY (Net Purchase Amount) - Model Construction Idea: NETBUY measures the net purchase amount of an ETF, indicating the flow of funds into or out of the ETF[2] - Model Construction Process: - Use the formula: $ NETBUY(T) = NAV(T) - NAV(T-1) * (1 + R(T)) $, where NAV(T) is the net asset value of the ETF on day T, and R(T) is the return of the ETF on day T[2] - Model Evaluation: NETBUY provides insights into investor sentiment and the demand for the ETF[2] Model Backtest Results RSI Model - RSI values for various ETFs: - Huatai-PB CSI 300 ETF: 58.20[4] - E Fund CSI 300 ETF: 53.92[4] - Huaxia CSI 300 ETF: 57.46[4] - China Southern CSI 500 ETF: 57.79[4] - Huaxia CSI 500 ETF: 57.62[4] - E Fund CSI 500 ETF: 57.58[4] - Huaxia SSE 50 ETF: 57.35[4] - E Fund SSE 50 ETF: 67.46[4] - China Southern CSI 1000 ETF: 58.00[4] - Huaxia CSI 1000 ETF: 58.42[4] - Huatai-PB CSI 2000 ETF: 61.76[4] - China Southern CSI 2000 ETF: 61.96[4] - Huaxia CSI 2000 ETF: 65.74[4] - Huatai-PB CSI A50 ETF: 56.96[4] - China Southern CSI A50 ETF: 56.38[4] - E Fund CSI A50 ETF: 56.85[4] - Huaxia CSI A100 ETF: 58.04[4] - China Southern CSI A100 ETF: 58.89[4] - China Southern CSI A500 ETF: 59.12[4] - Huaxia SSE STAR 50 ETF: 56.80[4] - E Fund SSE STAR 50 ETF: 57.74[4] - China Southern CSI STAR 100 ETF: 57.05[4] - Huaxia CSI STAR 100 ETF: 56.04[4] - E Fund GEM ETF: 55.45[4] - Huatai-PB GEM ETF: 55.44[4] - Huaxia Hang Seng Tech ETF: 55.49[4] - Huaxia Hang Seng ETF: 60.64[4] - Huaxia HK-HS ETF: 62.03[4] - Huaxia Nomura Nikkei 225 ETF: 61.12[4] - China Southern Nasdaq 100 ETF: 57.56[4] - China Southern S&P 500 ETF: 56.56[4] - Huatai-PB Saudi Arabia ETF: 40.43[4] - China Southern DAX ETF: 70.04[4] - China Southern CAC40 ETF: 61.34[4] NETBUY Model - Net Purchase Amount values for various ETFs: - Huatai-PB CSI 300 ETF: -18.85 billion[4] - E Fund CSI 300 ETF: -3.16 billion[4] - Huaxia CSI 300 ETF: -0.98 billion[4] - China Southern CSI 500 ETF: -1.73 billion[4] - Huaxia CSI 500 ETF: -0.18 billion[4] - E Fund CSI 500 ETF: -0.01 billion[4] - Huaxia SSE 50 ETF: -17.68 billion[4] - E Fund SSE 50 ETF: -0.99 billion[4] - China Southern CSI 1000 ETF: 4.79 billion[4] - Huaxia CSI 1000 ETF: 1.26 billion[4] - Huatai-PB CSI 2000 ETF: 0.63 billion[4] - China Southern CSI 2000 ETF: 0.15 billion[4] - Huaxia CSI 2000 ETF: 0.12 billion[4] - Huatai-PB CSI A50 ETF: -0.27 billion[4] - China Southern CSI A50 ETF: -0.63 billion[4] - E Fund CSI A50 ETF: -0.11 billion[4] - Huaxia CSI A100 ETF: 0.02 billion[4] - China Southern CSI A100 ETF: 0.00 billion[4] - China Southern CSI A500 ETF: -1.71 billion[4] - Huaxia SSE STAR 50 ETF: 6.81 billion[4] - E Fund SSE STAR 50 ETF: -0.64 billion[4] - China Southern CSI STAR 100 ETF: -0.14 billion[4] - Huaxia CSI STAR 100 ETF: 0.56 billion[4] - E Fund GEM ETF: 5.36 billion[4] - Huatai-PB GEM ETF: -0.13 billion[4] - Huaxia Hang Seng Tech ETF: 0.78 billion[4] - Huaxia Hang Seng ETF: -0.44 billion[4] - Huaxia HK-HS ETF: -0.13 billion[4] - Huaxia Nomura Nikkei 225 ETF: 0.16 billion[4] - China Southern Nasdaq 100 ETF: -0.62 billion[4] - China Southern S&P 500 ETF: -0.41 billion[4] - Huatai-PB Saudi Arabia ETF: 0.00 billion[4] - China Southern DAX ETF: 0.00 billion[4] - China Southern CAC40 ETF: 0.00 billion[4]