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]

- 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]