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]

Investment Rating - The report assigns a "Buy" rating to the company with a target price of HKD 57.27, based on a closing price of HKD 49.40 [6]. Core Insights - The report emphasizes that Leap Motor's success is driven by its commitment to self-research and development, leading to a strong product strategy that has helped the company stand out in the competitive market [2][15]. - Leap Motor achieved its first quarterly profit in Q4 2024, with a net profit of RMB 0.8 billion, marking it as the second profitable new energy vehicle manufacturer [3][46]. - The strategic partnership with Stellantis is highlighted as a significant advantage for Leap Motor's overseas expansion, providing unique resources and market access [4][46]. Summary by Sections 1. Self-Research and Product Strategy - Leap Motor has focused on self-research since its establishment in 2015, mastering a super-integrated technology architecture, which has been upgraded to LEAP 3.5 in 2025 [2][15]. - The company targets the mainstream passenger car market priced between RMB 100,000 and 200,000, with a projected total delivery of nearly 300,000 vehicles in 2024, ranking among the top three in new energy vehicle brands [2][21]. - Three key success factors are identified: efficient launch of range-extended models, continuous upgrades of existing models, and excellent cost control leading to competitive pricing [2][3][15]. 2. Profitability and Future Outlook - Leap Motor's revenue for 2024 reached RMB 32.16 billion, a year-on-year increase of 92%, with a gross margin of 8.4% [3][46]. - The company expects sustainable profitability due to a rich new vehicle planning and strong channel capabilities, which are anticipated to drive continuous sales growth [3][46]. - The report forecasts revenues of RMB 60.5 billion, RMB 80.5 billion, and RMB 106.8 billion for 2025, 2026, and 2027 respectively, with net profits projected to reach RMB 0.69 billion, RMB 22.52 billion, and RMB 43.92 billion in the same years [5][8]. 3. Overseas Business Development - The partnership with Stellantis, a major global automotive group, is expected to enhance Leap Motor's overseas business prospects significantly [4][46]. - The collaboration allows Leap Motor to leverage Stellantis's extensive dealer network and manufacturing resources, facilitating rapid market expansion in Europe and South America [4][46]. - The report suggests that local production could significantly improve profitability by reducing export costs and replicating successful domestic production practices [4][46].

1. Report Industry Investment Rating - No relevant content provided 2. Core Viewpoints of the Report - The market trading sentiment slightly recovered this week, with small - cap stocks outperforming. The A - share index is expected to maintain a wide - range volatile trend with support at the bottom and resistance at the top. It is recommended to maintain a barbell allocation of "dividend + growth" and pay attention to the phased investment opportunities in the pharmaceutical sector [10][11][16] 3. Summary by Related Catalogs 3.1 This Week's Market Review 3.1.1 Industry Index - The broad - market index fluctuated narrowly this week, with small - cap stocks performing strongly. The average daily trading volume of stocks increased by 3.43% compared to last week. Overseas, the Nasdaq rebounded, and the price of 10 - year US Treasury bonds rose. COMEX gold first soared and then corrected. The 30 - year Treasury bond futures remained in a high - level volatile pattern. The basis of four types of stock index futures contracts increased overall [10] - Sectors such as comprehensive finance, automobiles, and power equipment and new energy led the gains. The trading volume of the comprehensive finance and communication sectors increased significantly, while the trading activity of the agriculture, forestry, animal husbandry, and fishery, consumer services, and food and beverage sectors decreased significantly. The pharmaceutical sector, which was heavily added by active equity funds, rose by 1.24% [11] 3.1.2 Market Style - The growth style rebounded slightly, while the value style was relatively weak. The cycle style performed the best, with a weekly increase of 2.44%. The small - cap style was strong, with the CSI 2000 index having the highest increase [15][16] - The Shanghai Composite Index is expected to weakly rebound and oscillate up to around 3300 points in the short term, followed by a slight adjustment, but the downside space is limited [16] 3.2 Active Equity Funds 3.2.1 Top - Performing Funds in Different Theme Tracks This Week - Funds are classified into single - track and double - track funds based on their positions in six sectors: TMT, finance and real estate, consumption, medicine, manufacturing, and cycle [19] 3.2.2 Top - Performing Funds in Different Strategy Categories - Funds are divided into types such as deep - value, high - growth, high - quality, quality - growth, quality - value, GARP, and balanced - cost - performance funds, and the top - performing funds in each type this week are listed [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 0.48% and 0.41% respectively; for CSI 500 index - enhanced funds, they were 0.54% and 0.54%; for CSI 1000 index - enhanced funds, they were 0.62% and 0.69%; for ChiNext index - enhanced funds, they were 0.58% and 0.74%; for Science and Technology Innovation and Entrepreneurship 50 index - enhanced funds, they were 0.16% and 0.07%. The average and median absolute returns of neutral hedge funds were 0.26% and 0.18%, and for quantitative long - only funds, they were 1.67% and 1.71% [24] 3.4 This Issue's Bond Fund Selections - Medium - and long - term bond funds and short - term bond funds are selected based on indicators such as fund size, return - risk metrics, and rolling returns and maximum drawdowns over the past three years [37]

- The report introduces the **RSI (Relative Strength Index)** as a quantitative factor, constructed to measure market conditions of overbought or oversold states. 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] - Another quantitative metric discussed 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 provides detailed tracking of **ETF performance metrics**, including RSI values, net purchase amounts, and institutional holding percentages across various ETF categories such as broad-based indices (e.g., CSI 300, CSI 500) and thematic indices (e.g., non-bank financials, red-chip stocks)[4] - The report highlights **day-trading trends** using 5-minute interval price data to identify intraday highs and lows, represented by red dots on trend charts. However, some data gaps are noted due to missing intraday information[2] - The report categorizes ETFs into "broad-based" and "thematic" groups, analyzing their tracking indices, management fees, and liquidity metrics, such as trading volume and market capitalization[2][4] - Institutional holding percentages are estimated based on the latest annual or semi-annual reports, excluding holdings by linked funds. These values are subject to potential deviations[3] - The report includes **T+0 trading availability** for certain ETFs, indicating whether same-day buy-and-sell transactions are supported[3] - The report provides a comprehensive table of ETF metrics, including RSI values, net purchase amounts, institutional holding percentages, and other performance indicators for various ETFs across sectors like banking, semiconductors, renewable energy, and real estate[4][6]

Core Insights - The global macroeconomic environment is currently experiencing significant turbulence, with the US facing increased risks of stagflation while China's economic resilience is becoming evident [1] - The "reciprocal tariff" policy implemented by the Trump administration has disrupted global trade dynamics, heightening market uncertainty [1] - In this context, safe-haven assets like gold are gaining value, and A-shares are at historically low valuations, presenting investment opportunities [1] Global Macroeconomic Overview - The IMF forecasts global growth rates to remain at 3.3% for 2025-2026, below the historical average of 3.7% [2] - Inflation is generally on a downward trend, but service sector inflation remains sticky [2] - Divergence in global monetary policies is evident, with some central banks initiating rate cuts while others maintain a cautious stance [2] US Economic Conditions - As of March 2025, the US CPI year-on-year growth is at 2.4%, with core CPI at 2.8%, while service CPI remains high at 3.7% [3] - The budget deficit for the first half of FY 2025 exceeds $1.3 trillion, with federal debt to GDP surpassing 120% [3] - The "reciprocal tariff" policy aims to reduce trade deficits and promote manufacturing return, but it has led to significant market volatility and challenges to the dollar's credibility [3][19] China Economic Performance - In Q1 2025, China's GDP grew by 5.4% year-on-year, driven by strong exports, improving consumption, and proactive fiscal policies [5][43] - The core CPI turned positive in March, indicating initial policy effectiveness, although PPI remains in negative growth territory [5] - The real estate market shows signs of recovery, but continued policy support is necessary [5] Asset Class Performance and Outlook - The RMB exchange rate has limited depreciation space, with the central bank maintaining controllable risks [6] - The bond market presents more opportunities than risks, with expected easing policies starting in Q2 [6] - A-shares are currently attractive, with 22 out of 31 sectors having P/E ratios below the historical 50th percentile [6] - Gold has shown strong performance, with COMEX gold reaching $3341.3 per ounce, indicating long-term investment value during global uncertainty [6] Strategic Responses - There is a strong emphasis on boosting consumption through various measures, including increasing household income and optimizing the consumption environment [6] - Promoting the transformation of foreign trade enterprises is crucial to prevent excessive competition and cultivate new markets [6] - Accelerating the internationalization of the RMB is essential, focusing on regional cooperation and enhancing offshore market systems [6]