Market Liquidity Overview - R007 increased from 1.5811% to 1.591%, a rise of 0.99 basis points, while DR007 decreased from 1.502% to 1.4941%, a drop of 0.79 basis points. The spread between R007 and DR007 widened by 1.78 basis points [9][11] - The net outflow of funds this week was 320.904 billion, an increase of 391.818 billion from the previous week. Fund supply was 144.097 billion, and fund demand was 465.001 billion [11][12] Industry Sector Liquidity Tracking - Most sectors in the CITIC first-level industry recorded declines, with the banking sector showing the strongest performance, up 3.13%. The pharmaceutical and textile sectors led the declines, falling 4.16% and 4.10% respectively [16][17] - The pharmaceutical industry had the highest overall congestion score, indicating significant market activity and potential risk [27][28] Style Sector Liquidity Tracking - The consumer style index experienced the largest decline of 3.90%, while the financial style saw a slight increase of 0.05%, making it the only sector with positive returns [32][33] - The growth style maintained the highest daily trading volume share at 52.43%, significantly ahead of other styles, and also had the highest turnover rate at 2.33% [30][32]

证券研究报告—ETF 基金周报 撰写日期：2025 年 06 月 23 日 ETF 周报（20250616-20250620） 摘要 ETF 新发及上市情况： 在样本期内一共有 10 只基金成立，6 只基金上 市。 风险提示：本报告对于基金产品、指数的研究分析均基于历史公开信 息，可能受指数样本股的变化而产生一定的分析偏差；此外，基金管理人的 历史业绩与表现不代表未来；指数未来表现受宏观环境、市场波动、风格转 换等多重因素影响，存在波动风险；本报告不涉及证券投资基金评价业务， 不涉及对基金产品的推荐，亦不涉及对任何指数样本股的推荐。 麦高证券 研究发展部 分析师：林永绿 资格证书：S0650524060001 联系邮箱：linyonglv@mgzq.com 联系电话：15000307034 联系人：张昊阳 资格证书：S0650124040024 联系邮箱：zhanghaoyang@mgzq.com 联系电话：13363378283 相关研究 《麦高视野--ETF 观察日志（20250620）》 2025.06.23 《麦高视野--ETF 观察日志（20250619）》 2025.06.20 《麦高视野--ET ...

ETF Performance Overview - The Huatai-PB CSI 300 ETF has a market value of CNY 368.77 billion and a decline of 0.79% with an RSI of 30.88, indicating a near oversold condition[4] - The E Fund CSI 300 ETF also shows a decline of 0.79% with a market value of CNY 260.36 billion and an RSI of 45.05, suggesting moderate market strength[4] - The CSI 500 ETFs, such as the Southern CSI 500 ETF, have experienced a decline of 1.00% with a market value of CNY 109.87 billion and an RSI of 44.88, indicating a weak market[4] Market Trends and Indicators - The overall market sentiment is reflected in the RSI values, with several ETFs showing RSI below 30, indicating oversold conditions, such as the Tianhong CSI 300 ETF with an RSI of 45.32[4] - Net purchases for the Huatai-PB CSI 300 ETF were negative at CNY -5.49 billion, indicating a trend of selling pressure[4] - The trading volume for the Huatai-PB CSI 300 ETF was CNY 23.31 billion, reflecting active trading despite the price decline[4] Sector-Specific Insights - The Consumer Electronics sector ETFs, such as the Huaxia National Index Consumer Electronics ETF, have shown a decline of 0.63% with an RSI of 55.27, indicating a relatively stable market[6] - The Non-Bank sector ETFs, including the E Fund Non-Bank ETF, have experienced a decline of 1.66% with an RSI of 47.50, suggesting a cautious outlook[6] - The New Energy sector ETFs, like the Southern CSI New Energy ETF, have shown a decline of 1.16% with an RSI of 44.37, indicating potential weakness in this sector[6]

Group 1: Financial Data Overview - In May 2025, the total social financing increased by 22,894 billion yuan, which is 2,271 billion yuan more than the same period last year[2] - The stock growth rate of social financing recorded 8.7%, remaining unchanged from the previous value[2] - New RMB loans in May amounted to 6,200 billion yuan, which was lower than expected, indicating a need for improved effective financing demand[2] Group 2: Government Financing and Loan Trends - Government bonds increased by 14,633 billion yuan in May, reflecting a year-on-year increase of 2,367 billion yuan, supporting social financing expansion[9] - Corporate loans increased by 5,300 billion yuan, but this was a year-on-year decrease of 2,100 billion yuan, influenced by global trade tensions[10] - Resident loans increased by 540 billion yuan, but this also represented a year-on-year decrease of 217 billion yuan, showing weak leverage willingness post-interest rate cuts[10] Group 3: Monetary Supply and Policy Implications - M2 growth rate in May recorded 7.9%, a slight decrease of 0.1 percentage points from the previous month, likely due to slowed credit expansion[14] - M1 growth rate improved by 0.8 percentage points to 2.3%, reflecting the impact of recent financial support policies on market confidence[14] - Future strategies should focus on enhancing fiscal efforts and coordinating monetary policy to stimulate financing willingness in the real economy[19] Group 4: Risks and Challenges - Risks include potential underperformance of policy implementation, slower-than-expected economic recovery, and unexpected developments in US-China trade tensions[21]

Market Liquidity Overview - R007 increased from 1.5514% to 1.5811%, a rise of 2.97 basis points, while DR007 decreased from 1.5323% to 1.502%, a drop of 3.03 basis points, resulting in a widening spread of 6.00 basis points between R007 and DR007 [9][11] - The net inflow of funds this week was 709.14 billion, an increase of 142.27 billion from the previous week, with total fund supply at 1323.94 billion and demand at 614.80 billion [11][12] Industry Sector Liquidity Tracking - The performance of the CITIC first-level industries was mixed, with non-ferrous metals and oil & petrochemicals leading gains at approximately 3.95% and 3.31% respectively, while food & beverage and computer sectors saw the largest declines at 4.42% and 2.25% respectively [16][18] - The pharmaceutical sector exhibited high overall congestion, with financials showing high congestion from volatility and turnover rates, while non-ferrous metals displayed high congestion from a momentum perspective, indicating elevated risk [29][30] Style Sector Liquidity Tracking - Growth style maintained the highest daily trading volume share at 48.79%, while financial and cyclical styles saw slight increases in their trading volume shares [32][33] - Daily turnover rates for growth style remained the highest at 2.48%, whereas financial and stable styles maintained relatively low turnover rates [32]

证券研究报告—ETF 基金周报 撰写日期：2025 年 06 月 16 日 ETF 周报（20250609-20250613） 摘要 二级市场概况：统计 A 股、海外主要宽基指数、黄金指数和南华商 品指数的走势，样本期内南华商品指数、SGE 黄金 9999 和恒生指数本周收 益排名靠前，分别为 2.14%、1.56%和 0.42%。计算所有申万一级行业在样本 期内的收益情况，其中有色金属、石油石化和农林牧渔的收益排名靠前，分 别为 3.79%、3.50%和 1.62%，食品饮料、家用电器和建筑材料的收益排名较 为靠后，分别为-4.37%、-3.26%和-2.77%。 ETF 资金流情况：从不同类别 ETF 角度来看，债券 ETF 的资金净流入 最多，为 143.91 亿元；宽基 ETF 的资金净流入最少，为-106.32 亿元。从 ETF 跟踪指数及其成分股上市板块角度来看，港股 ETF 的资金净流入最多， 为 42.32 亿元；创业板相关 ETF 的资金净流入最少，为-34.04 亿元。从行 业板块角度来看，周期板块 ETF 的资金净流入最多，为 7.86 亿元；生物医 药板块 ETF 的资金净流入最少，为 ...

Quantitative Models and Construction Methods Model Name: RSI (Relative Strength Index) - Construction Idea: RSI measures the magnitude of recent price changes to evaluate overbought or oversold conditions in the price of a stock or other asset[2] - Detailed 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 average gain divided by the average loss[2] - Evaluation: RSI > 70 indicates the market is in an overbought condition, while RSI < 30 indicates the market is in an oversold condition[2] Model Name: NETBUY (Net Purchase Amount) - Construction Idea: NETBUY calculates the net purchase amount of an ETF based on its net asset value (NAV) and return[2] - Detailed Construction Process: - Use the formula: $ NETBUY(T) = NAV(T) - NAV(T-1) * (1 + R(T)) $ where NETBUY(T) is the net purchase amount, 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: - Huatai-PB CSI 300 ETF: 59.63[4] - E Fund CSI 300 ETF: 59.08[4] - ChinaAMC CSI 300 ETF: 59.30[4] - Harvest CSI 300 ETF: 58.93[4] - Tianhong CSI 300 ETF: 58.51[4] - Southern CSI 500 ETF: 61.53[4] - ChinaAMC CSI 500 ETF: 61.49[4] - Harvest CSI 500 ETF: 60.75[4] - E Fund CSI 500 ETF: 61.56[4] - ChinaAMC SSE 50 ETF: 52.77[4] - E Fund SSE 50 ETF: 54.31[4] - CCB SSE 50 ETF: 50.97[4] - Huatai-PB CSI 2000 ETF: 61.30[4] - Southern CSI 2000 ETF: 62.21[4] - ChinaAMC CSI 2000 ETF: 64.83[4] - Ping An CSI A50 ETF: 52.58[4] - Dacheng CSI A50 ETF: 53.33[4] - E Fund CSI A50 ETF: 53.38[4] - ChinaAMC CSI A100 ETF: 54.52[4] - GF CSI A100 ETF: 53.52[4] - Guotai CSI A500 ETF: 58.13[4] - Southern CSI A500 ETF: 58.98[4] - GF CSI A500 ETF: 58.87[4] - ChinaAMC SSE STAR 50 ETF: 40.85[4] - E Fund SSE STAR 50 ETF: 41.06[4] - ICBC SSE STAR 50 ETF: 41.47[4] - Bosera SSE STAR 100 ETF: 56.52[4] - Penghua SSE STAR 100 ETF: 56.74[4] - ChinaAMC SSE STAR 100 ETF: 57.03[4] - E Fund GEM ETF: 59.93[4] - Tianhong GEM ETF: 59.90[4] - GF GEM ETF: 60.01[4] - ChinaAMC Hang Seng Tech ETF: 54.81[4] - ChinaAMC Hang Seng ETF: 63.75[4] - ChinaAMC HK-Hang Seng ETF: 63.63[4] - Huaan Mitsubishi UFJ Nikkei 225 ETF: 63.21[4] - ChinaAMC Nomura Nikkei 225 ETF: 62.65[4] - GF Nasdaq 100 ETF: 59.68[4] - Guotai Nasdaq 100 ETF: 60.09[4] - Bosera S&P 500 ETF: 63.67[4] - Penghua Dow Jones Industrial Average ETF: 58.87[4] - Huatai-PB Southern East Asia Saudi Arabia ETF: 44.60[4] - Huaan Germany (DAX) ETF: 51.84[4] - Huaan France CAC40 ETF: 56.31[4] Quantitative Factors and Construction Methods Factor Name: RSI (Relative Strength Index) - Construction Idea: RSI measures the magnitude of recent price changes to evaluate overbought or oversold conditions in the price of a stock or other asset[2] - Detailed 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 average gain divided by the average loss[2] - Evaluation: RSI > 70 indicates the market is in an overbought condition, while RSI < 30 indicates the market is in an oversold condition[2] Factor Name: NETBUY (Net Purchase Amount) - Construction Idea: NETBUY calculates the net purchase amount of an ETF based on its net asset value (NAV) and return[2] - Detailed Construction Process: - Use the formula: $ NETBUY(T) = NAV(T) - NAV(T-1) * (1 + R(T)) $ where NETBUY(T) is the net purchase amount, 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] Factor Backtesting Results - RSI Factor, RSI values for various ETFs: - Huatai-PB CSI 300 ETF: 59.63[4] - E Fund CSI 300 ETF: 59.08[4] - ChinaAMC CSI 300 ETF: 59.30[4] - Harvest CSI 300 ETF: 58.93[4] - Tianhong CSI 300 ETF: 58.51[4] - Southern CSI 500 ETF: 61.53[4] - ChinaAMC CSI 500 ETF: 61.49[4] - Harvest CSI 500 ETF: 60.75[4] - E Fund CSI 500 ETF: 61.56[4] - ChinaAMC SSE 50 ETF: 52.77[4] - E Fund SSE 50 ETF: 54.31[4] - CCB SSE 50 ETF: 50.97[4] - Huatai-PB CSI 2000 ETF: 61.30[4] - Southern CSI 2000 ETF: 62.21[4] - ChinaAMC CSI 2000 ETF: 64.83[4] - Ping An CSI A50 ETF: 52.58[4] - Dacheng CSI A50 ETF: 53.33[4] - E Fund CSI A50 ETF: 53.38[4] - ChinaAMC CSI A100 ETF: 54.52[4] - GF CSI A100 ETF: 53.52[4] - Guotai CSI A500 ETF: 58.13[4] - Southern CSI A500 ETF: 58.98[4] - GF CSI A500 ETF: 58.87[4] - ChinaAMC SSE STAR 50 ETF: 40.85[4] - E Fund SSE STAR 50 ETF: 41.06[4] - ICBC SSE STAR 50 ETF: 41.47[4] - Bosera SSE STAR 100 ETF: 56.52[4] - Penghua SSE STAR 100 ETF: 56.74[4] - ChinaAMC SSE STAR 100 ETF: 57.03[4] - E Fund GEM ETF: 59.93[4] - Tianhong GEM ETF: 59.90[4] - GF GEM ETF: 60.01[4] - ChinaAMC Hang Seng Tech ETF: 54.81[4] - ChinaAMC Hang Seng ETF: 63.75[4] - ChinaAMC HK-Hang Seng ETF: 63.63[4] - Huaan Mitsubishi UFJ Nikkei 225 ETF: 63.21[4] - ChinaAMC Nomura Nikkei 225 ETF: 62.65[4] - GF Nasdaq 100 ETF: 59.68[4] - Guotai Nasdaq 100 ETF: 60.09[4] - Bosera S&P 500 ETF: 63.67[4] - Penghua Dow Jones Industrial Average ETF: 58.87[4] - Huatai-PB Southern East Asia Saudi Arabia ETF: 44.60[4] - Huaan Germany (DAX) ETF: 51.84[4] - Huaan France CAC40 ETF: 56.31[4]

Inflation Data Summary - In May, the CPI decreased by 0.2% month-on-month and recorded -0.1% year-on-year, remaining in the negative growth range[2] - Core CPI remained flat month-on-month and increased by 0.6% year-on-year, indicating the effectiveness of consumption-boosting policies[2] - Food prices fell by 0.4% year-on-year, with a month-on-month decrease of 0.2%[2] - Seasonal vegetable supply increased, leading to a 5.9% drop in fresh vegetable prices, while fresh fruit prices rose by 3.3% due to supply constraints[13] Producer Price Index (PPI) Insights - The PPI continued to decline, recording -3.3% year-on-year and -0.4% month-on-month[3] - International commodity prices fell sharply, impacting sectors like oil and gas extraction, which saw a price drop of 5.6%[20] - Consumer goods prices showed some recovery, with clothing and durable goods prices increasing by 0.2% and 0.1% respectively[20] - New energy sectors like photovoltaics and lithium batteries experienced a narrowing of price declines, with reductions of -12.1% and -5.0% respectively[21] Economic Outlook - Overall inflation data indicates a low operating level, with both CPI and PPI in negative growth ranges, reflecting insufficient effective demand in the economy[23] - Despite short-term pressure from food and energy prices, core CPI stabilization and structural improvements suggest that policy measures are gradually taking effect[23] - The monetary policy is expected to remain flexible and appropriate, potentially utilizing tools like reserve requirement ratio cuts and interest rate reductions to stabilize domestic demand and market expectations[5]

Report Industry Investment Rating No relevant content provided. Core Viewpoints The report comprehensively analyzes the secondary market and ETF product situation from multiple perspectives, including the performance of major indices, the inflow and outflow of ETF funds, trading volume, margin trading, and new fund issuance and listing. It provides a detailed overview of the market trends and characteristics during the sample period from June 3, 2025, to June 6, 2025 [1][2][3]. Summary by Directory 1. Secondary Market Overview - In the sample period, the Hang Seng Index, ChiNext Index, and CSI 2000 had the highest weekly returns, at 2.74%, 2.32%, and 2.29% respectively. The PE valuation quantile of CSI 2000 was the highest at 97.10%, while that of the Nikkei 225 was the lowest at 28.69% [10]. - Among the Shenwan primary industries, Communications, Non - Ferrous Metals, and Electronics had the highest returns, at 5.27%, 3.74%, and 3.60% respectively. Household Appliances, Food and Beverage, and Transportation had the lowest returns, at - 1.79%, - 1.06%, and - 0.54% respectively [15]. - The industries with the highest valuation quantiles were Building Decoration, Light Industry Manufacturing, and Banking, at 99.59%, 99.59%, and 99.17% respectively. The industries with the lowest valuation quantiles were Comprehensive, Agriculture, Forestry, Animal Husbandry and Fishery, and Non - Banking Finance, at 7.05%, 8.71%, and 12.45% respectively [17]. 2. ETF Product Overview 2.1 ETF Market Performance - By product type, industry - themed ETFs had the best average performance with a weighted average return of 2.08%, while money market ETFs had the worst performance with a weighted average return of 0.00% [22]. - By listing board, Hong Kong - related and ChiNext - related ETFs had better performance, with weighted average returns of 2.98% and 2.25% respectively. Japanese - stock and MSCI China A - share concept ETFs had poorer performance, with weighted average returns of - 0.71% and 0.52% respectively [22]. - By industry sector, technology sector ETFs had the best average performance with a weighted average return of 3.46%, while consumer sector ETFs had the worst performance with a weighted average return of - 0.10% [26]. - By theme, AI and consumer electronics ETFs had better performance, with weighted average returns of 4.25% and 3.72% respectively. Military and low - carbon environmental protection ETFs had relatively poor performance, with weighted average returns of 0.05% and 0.31% respectively [26]. 2.2 ETF Fund Inflow and Outflow - By ETF category, bond ETFs had the largest net inflow of funds at 15.902 billion yuan, while broad - based ETFs had the smallest net inflow at - 6.059 billion yuan [29]. - By tracking index and listing board, CSI 300 ETFs had the largest net inflow of funds at 737 million yuan, while Hong Kong - stock ETFs had the smallest net inflow at - 4.591 billion yuan [29]. - By industry sector, technology sector ETFs had the largest net inflow of funds at 6.303 billion yuan, while biomedical sector ETFs had the smallest net inflow at - 1.59 billion yuan [31]. - By theme, military and dividend ETFs had the largest net inflows of funds, at 1.359 billion yuan and 588 million yuan respectively. Non - banking and innovative drug ETFs had the smallest net inflows, at - 999 million yuan and - 665 million yuan respectively [31]. 2.3 ETF Trading Volume - By ETF category, bond ETFs had the largest increase in average daily trading volume change rate at 8.75%, while commodity ETFs had the largest decrease at - 30.78% [36]. - By tracking index and listing board, Hong Kong - stock ETFs had the largest increase in average daily trading volume change rate at 4.07%, while US - stock ETFs had the largest decrease at - 39.68% [38]. - By industry sector, the biomedical sector had the largest increase in average daily trading volume change rate at 22.63%, while the traditional manufacturing sector had the largest decrease at - 16.55% [41]. - By theme, chip semiconductor and innovative drug ETFs had the largest average daily trading volumes in the past 5 days, at 3.315 billion yuan and 2.764 billion yuan respectively. Innovative drug and chip semiconductor ETFs had the largest increases or smallest decreases in average daily trading volume change rate, at 39.80% and 2.91% respectively. New energy and low - carbon environmental protection ETFs had the largest decreases or smallest increases in average daily trading volume change rate, at - 26.45% and - 26.36% respectively [45]. 2.4 ETF Margin Trading - In the sample period, the net margin purchase of all equity ETFs was - 638 million yuan, and the net short - selling was 234 million yuan. GF CSI Hong Kong Stock Connect Automobile Industry Theme ETF had the largest net margin purchase, and Southern CSI 500 ETF had the largest net short - selling [3][50]. 2.5 ETF New Issuance and Listing - During the sample period, a total of 7 funds were established and 6 funds were listed [3][52].

- 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]