Liquidity Overview - The average daily lending level of banks decreased from 5.0 trillion yuan to 4.3 trillion yuan, with a low of 3.7 trillion yuan on Friday[2] - The central bank conducted a net withdrawal of 2,250 billion yuan on July 7, marking the end of the cross-season liquidity support[1] - The overnight and 7-day funding rates rose by 1-2 basis points compared to the previous week due to continued net withdrawal pressure[1] Market Trends - As of July 11, the R001 rate increased by 4.3 basis points to 1.40%, while the R007 rate rose by 2.1 basis points to 1.51%[1] - The issuance rate for 1-year certificates of deposit from state-owned banks increased from 1.59% to 1.62% during the week[2] - The weighted issuance rate for interbank certificates of deposit was 1.61%, down 1 basis point from the previous week[7] Future Outlook - The upcoming tax period (July 14-18) is expected to cause fluctuations in funding prices, with overnight rates potentially ranging between OMO and OMO+5 basis points[3] - The central bank's attitude remains supportive, as indicated by the resumption of reverse repos on July 10-11[3] - The stability of funding rates during the tax period will depend on the central bank's medium- and long-term fund injections[3] Government Bonds - Net payments for government bonds increased to 398.5 billion yuan for the period of July 14-18, with both national and local bonds seeing an increase in net payments[6] - The planned issuance of government bonds for the same period is 4,642 billion yuan, slightly down from the previous week[6] Interbank Certificates of Deposit - The pressure from maturing certificates of deposit is increasing, with 7,822 billion yuan maturing from July 14-18, up from 5,213 billion yuan the previous week[7] - The average weighted maturity of interbank certificates of deposit decreased to 8.9 months from 9.7 months[7]

Report Industry Investment Rating No relevant content provided. Core View - The real - estate market shows a weak trend this week. Second - hand housing transactions have declined both in terms of week - on - week and year - on - year, and new housing transactions have also decreased significantly, especially affected by the high base of the previous week's quarterly - end impulse [1][3] Summary by Related Content Second - hand Housing Transaction - **Overall Situation**: This week (July 4 - 10), the transaction area of second - hand housing in 15 cities was 1.99 million square meters, hitting a low point this year (excluding holidays). The week - on - week decline was 5%, and the year - on - year decline was also 5%. Since early July, the cumulative year - on - year decline has been 6%, larger than the 3% decline in June [1] - **By City Level**: - **First - tier Cities**: The weekly transaction area of second - hand housing has changed from an increase to a decrease, with a week - on - week decline of 20%. Year - on - year, it has declined for five consecutive weeks, with a 7% decline this week. Among them, Beijing, Shanghai, and Shenzhen declined by 32%, 14%, and 5% respectively week - on - week, and Beijing and Shanghai declined by 21% and 5% year - on - year, while Shenzhen increased by 22% [2] - **Second - tier and Third - tier Cities**: The weekly transaction area of second - hand housing has changed from a decrease to an increase, with week - on - week growth of 3% and 21% respectively. Year - on - year, second - tier cities declined by 8%, and third - tier cities increased by 9% [2] New Housing Transaction - **Overall Situation**: This week, the transaction area of new housing in 38 cities was 2.08 million square meters, with a week - on - week decline of 55% due to the high base of the previous week's quarterly - end impulse. Year - on - year, it has declined for five consecutive weeks, with a 17% decline this week. Since early July, the cumulative year - on - year decline has been 23% [3] - **By City Level**: - **First - tier Cities**: The weekly transaction area of new housing has changed from an increase to a decrease, with a week - on - week decline of 49%. Year - on - year, it has declined for five consecutive weeks, with an 11% decline this week. Among them, Beijing, Shanghai, Guangzhou, and Shenzhen declined by 26%, 64%, 57%, and 28% respectively week - on - week. Beijing increased by 59% year - on - week, while Shanghai, Shenzhen, and Guangzhou declined by 39%, 53%, and 20% respectively [3] - **Second - tier and Third - tier Cities**: The weekly transaction area of new housing has also changed from an increase to a decrease, with week - on - week declines of 56% and 59% respectively. Year - on - year, second - tier and third - tier cities declined by 24% and 13% respectively [4] Key City Observation - **First - tier Cities**: - **Second - hand Housing**: From July 4 - 10, the weekly transaction area of second - hand housing in first - tier cities changed from an increase to a decrease week - on - week, and declined year - on - year for five consecutive weeks. Beijing, Shenzhen, and Shanghai declined by 32%, 5%, and 14% respectively week - on - week. In terms of year - on - week, Shanghai and Beijing declined, while Shenzhen increased. In terms of month - on - month (July 1 - 10), Beijing and Shanghai declined, while Shenzhen increased [24] - **New Housing**: The weekly transaction area of new housing in first - tier cities declined by 49% week - on - week. All four cities declined. In terms of year - on - week, Beijing increased, while Shanghai, Shenzhen, and Guangzhou declined. In terms of month - on - month (July 1 - 10), Shanghai, Shenzhen, and Guangzhou declined, while Beijing was basically flat [24][25] - **Hangzhou**: This week, the weekly transaction areas of second - hand and new housing declined by 7% and 80% respectively compared with the previous week, equivalent to 50% and 19% of the 2024 high points [25] - **Chengdu**: This week, the weekly transaction areas of second - hand and new housing increased by 4% and 13% respectively compared with the previous week, equivalent to 57% and 42% of the 2024 high points [25] Housing Price Observation - From June 30 to July 6, the weekly listed prices of second - hand housing in Shanghai, Beijing, and Shenzhen declined by 0.70%, 0.18%, and 0.41% respectively week - on - week. Compared with the week before the "924" policy last year, they still declined by 1.5%, 6.0%, and 4.9% respectively [51]

Group 1: A-share Market Valuation - The current PE (TTM) of the A-share market is 15.94, with a historical average of 22.57 when excluding financial and oil sectors[7] - The Shanghai Composite Index has a PE (TTM) of 13.25, while the ChiNext Index stands at 49.22, indicating significant valuation differences among indices[15] - The risk premium for the A-share market is currently at 0.79%, which is below the historical average[18] Group 2: Hong Kong Market Valuation - The Hang Seng Index has a current PE (TTM) of 10.75, with a historical maximum of 22.67 and a minimum of 7.36[58] - The Hang Seng Technology Index shows a current PE (TTM) of 19.81, reflecting a higher valuation compared to the broader market[63] - The Hang Seng Financial Index has a median PE of 7.45, indicating lower valuations in the financial sector[70] Group 3: U.S. Market Valuation - The S&P 500 Index has a current PE (TTM) of 28.02, with a historical maximum of 41.99 and a minimum of 11.21[80] - The NASDAQ Index shows a PE (TTM) of 42.43, indicating a high valuation relative to historical levels[88] - The Dow Jones Industrial Average has a current PE (TTM) of 31.24, reflecting strong market performance[92] Group 4: Sector Valuation Insights - In the A-share market, sectors like non-ferrous metals and food & beverage are currently at historically low PE levels, while sectors like computers and steel are at high PE levels[25] - The banking sector in A-shares has a PB (LF) of 0.64, indicating undervaluation compared to historical averages[28] - The consumer sector, particularly in white goods, has a PE (TTM) of 11.02, suggesting potential for growth[37]

Global Market Overview - The report indicates that global markets are under pressure due to current tariff issues, leading to increased volatility. Major US stock indices experienced pullbacks, with the S&P 500, Nasdaq, and Dow Jones all declining [1][3] - The TAMAMA technology index's price-to-earnings (P/E) ratio has risen to 35.1, exceeding the 35 mark, indicating a high valuation. The Philadelphia Semiconductor Index's P/E ratio has further increased to 51.8, while the Nasdaq's P/E ratio stands at 42.5, both suggesting potential overvaluation [1][12] - The report highlights that the Shiller P/E ratio for the S&P 500 is at 38.12, significantly above historical averages, indicating that various sectors such as finance, consumer, communication services, and industrials may face corrections due to high valuations and economic uncertainties [1][12] US Market Performance - The S&P 500 index, Nasdaq, and Dow Jones Industrial Average all saw declines of 0.31%, 0.08%, and 1.02% respectively during the week [3][12] - Within the S&P 500, the energy sector had the highest increase at 2.48%, while the financial sector experienced the largest decline at 1.91% [12][16] European Market Performance - European markets showed mixed results, with the German DAX index increasing by 1.97%, while other indices like the UK FTSE 100 and French CAC40 also saw modest gains [9][10] - The report anticipates potential corrections in major European indices such as the CAC40, FTSE 100, DAX, and others due to high price-to-book ratios and economic pressures [1][9] Hong Kong Market Performance - The Hang Seng Index, Hang Seng China Enterprises Index, and Hang Seng Hong Kong Chinese Enterprises Index all increased, with respective gains of 0.93%, 0.91%, and 2.07% [4][24] - The report notes that the Hong Kong market is expected to experience further differentiation, with low-valuation assets that are less impacted by trade issues presenting structural buying opportunities amidst volatility [1][39] Emerging Markets Performance - Emerging markets displayed varied performance, with the Ho Chi Minh Index rising by 5.1%, while the Brazilian IBOVESPA index fell by 3.59% [11][39] - The report suggests that emerging markets may also face corrections due to economic fundamentals and uncertainties stemming from US trade policies [1][39] Key Economic Data - The report mentions that in May 2025, the Eurozone retail sales index grew by 1.8%, down from 2.7% previously, indicating a slowdown in consumer spending [4][43] - In June 2025, Germany's CPI year-on-year growth was 2%, slightly lower than the previous 2.1%, while France's CPI increased to 1% from 0.7% [40][43]

Quantitative Models and Construction Methods 1. Model Name: Industry Investment Radar - **Model Construction Idea**: The model identifies four states of industry trends (volume increase with price rise, volume increase with price drop, volume decrease with price drop, and volume decrease with price rise) based on the direction of price and trading volume changes. These states are visualized in a polar coordinate system to locate investment opportunities when industries move into specific regions of the radar[7][8][11] - **Model Construction Process**: 1. **State Classification in Cartesian Coordinates**: - Price and trading volume changes are categorized into four states: - Volume increase with price rise (Quadrant 1) - Volume increase with price drop (Quadrant 2) - Volume decrease with price drop (Quadrant 3) - Volume decrease with price rise (Quadrant 4)[11] 2. **Polar Coordinate Transformation**: - **Polar Angle**: Calculated using the arctangent function to represent the ratio of trading volume change to price change $ \theta = \arctan2(\text{Volume Change}, \text{Price Change}) $[14][18] - **Polar Radius**: Calculated using the Mahalanobis distance to measure the distance between the current and historical price-volume data $ \rho = \sqrt{(x-y)^T \cdot \Sigma^{-1} \cdot (x-y)} $ where $x$ is the current price-volume vector, $y$ is the historical price-volume vector, and $\Sigma$ is the covariance matrix[13][14] 3. **State Mapping in Polar Coordinates**: - Quadrants are mapped to specific polar angle ranges: - 0°-90°: Volume increase with price rise - 90°-180°: Volume increase with price drop - 180°-270°: Volume decrease with price drop - 270°-360°: Volume decrease with price rise[17][18] - **Model Evaluation**: The model provides a clear and interpretable framework for identifying industry rotation opportunities, leveraging historical price-volume relationships to predict future performance[8][18] 2. Model Name: Position Parameter Table - **Model Construction Idea**: This model establishes a mapping between historical price-volume states and future returns by dividing the polar coordinate space into regions and calculating the average future returns for each region[29][38] - **Model Construction Process**: 1. **Region Division**: - The polar radius is divided into five equal segments, and the polar angle is divided into 16 equal regions, resulting in 80 distinct regions[29] 2. **Return Mapping**: - For each region, the average future 20-day return is calculated based on historical data[29][38] 3. **Multi-Dimensional Expansion**: - **Dimension 1**: Multiple historical periods are analyzed for their relationship with future 20-day returns[47] - **Dimension 2**: Multiple historical dates are aggregated to identify stable investment regions[45] - **Model Evaluation**: The position parameter table enhances the model's robustness by incorporating multi-period and multi-date data, providing a more comprehensive mapping of historical states to future returns[47][50] --- Model Backtesting Results 1. Industry Investment Radar - **Weekly Rebalancing Portfolio**: - Cumulative Return: 369.06% - Benchmark Return: 80.97% - Excess Return: 288.09%[56] - **Monthly Rebalancing Portfolio**: - Cumulative Return: 388.85% - Benchmark Return: 80.97% - Excess Return: 307.88%[59] - **Semi-Annual Rebalancing Portfolio**: - Cumulative Return: 279.77% - Benchmark Return: 80.97% - Excess Return: 198.80%[60] 2. Position Parameter Table - **Future 20-Day Return Mapping**: - Example Regions: - Polar Radius (1/5, 2/5), Polar Angle (4π/8, 5π/8): 5.55% - Polar Radius (0, 1/5), Polar Angle (-5π/8, -4π/8): 4.64% - Polar Radius (1/5, 2/5), Polar Angle (-6π/8, -5π/8): 4.09%[42][44] --- Quantitative Factors and Construction Methods 1. Factor Name: Price-Volume State Factor - **Factor Construction Idea**: This factor captures the relationship between price and trading volume changes to classify industry states and predict future returns[7][8][11] - **Factor Construction Process**: - Derived from the polar coordinate transformation of price and volume data, incorporating both polar radius and polar angle as key metrics[13][14][18] - **Factor Evaluation**: The factor is intuitive and interpretable, effectively linking historical price-volume dynamics to future performance[8][18] 2. Factor Name: Regional Return Factor - **Factor Construction Idea**: This factor quantifies the average future returns of industries based on their historical positions in the polar coordinate system[29][38] - **Factor Construction Process**: - Calculated as the average future 20-day return for each region in the position parameter table[29][38] - **Factor Evaluation**: The factor provides a systematic approach to identifying high-return regions, leveraging historical data to enhance predictive accuracy[45][47] --- Factor Backtesting Results 1. Price-Volume State Factor - **Future 20-Day Return Examples**: - Polar Radius (2/5, 3/5), Polar Angle (5π/8, 6π/8): 3.51% - Polar Radius (0, 1/5), Polar Angle (4π/8, 5π/8): 2.49%[42][44] 2. Regional Return Factor - **Future 20-Day Return Examples**: - Polar Radius (3/5, 4/5), Polar Angle (6π/8, 7π/8): -3.06% - Polar Radius (0, 1/5), Polar Angle (3π/8, 4π/8): -3.95%[42][44]

证券研究报告|宏观点评报告 [Table_Date] 2025 年 07 月 10 日 [Table_Title] 资产配置日报：憧憬 2015 重现 | | | 7 月 10 日，"反内卷"叙事余音未尽，棚改重启预期成为新的主线，股市普涨，债市齐跌，部分商品迎来强势 反弹行情。 国内资产方面，股市，大盘稳中有升，上证指数、沪深 300、中证红利分别上涨 0.48%、0.47%、0.62%；科 技很快仍在调整，科创 50、恒生科技下跌 0.32%、0.29%；小微盘行情相对平淡，中证 2000 下跌 0.05%，万得 微盘股指上涨 0.01%。债市，10 年、30 年国债收益率分别上行 1.45bp、1.60bp 至 1.66%、1.88%；10 年、30年 国债期货主力合约下跌 0.16%、0.36%。 海外方面，美国新一轮关税交易情绪整体进入缓和阶段，美元指数延续震荡走势，黄金价格亦维持窄幅波 动，日内振幅控制在 0-0.5%区间。不过，随着 8 日特朗普提出对进口铜加征 50%关税，铜价波动较大，COMEX 铜价昨日下跌 2.5%后，今日开盘后明显反弹，涨幅一度超过 2%，LME 铜亦表现偏强，日内上涨 ...

1. Report Industry Investment Rating No information provided in the given content. 2. Core View of the Report - In the low - interest - rate era, "extending duration" and "controlling costs" are two key strategies for bond investment. The public - offering bond funds are shifting from "credit downgrading" to "duration management", and the low - cost advantage of bond - fund indexation is prominent [1][2]. - Long - duration index bond funds are the best combination of "high duration" and "low cost". Different types of long - duration index bond funds have different risk - return characteristics, and investors can choose according to their needs [3]. - Ten - year Treasury bond ETFs are effective "offensive - and - defensive" tools for enhancing portfolio returns, and are also suitable for short - term trading and rotation strategies [7]. 3. Summary According to the Directory 3.1 Low - Interest - Rate Era: "Extending Duration" and "Controlling Costs" as Two Key Strategies - Since early 2014, domestic interest rates have been in a downward cycle, and the upward elasticity of interest rates has weakened. From 2015 to 2025, the market has experienced five rounds of local government debt resolution, gradually flattening the credit spread [1][12][13]. - Facing the "low - interest - rate + low - spread" environment, public - offering bond funds are shifting from "credit downgrading" to "duration management". The average allocation ratio of medium - and long - term bond funds to credit bonds has dropped from 41% in 2020 to 30% at the end of Q1 2025, while the acceptance of portfolio duration is increasing [1][20][21]. - Referring to overseas experience, in a low - interest - rate environment, Japanese public - offering bond funds have increased their allocation to long - term bonds. In terms of risk - return ratio, the cost - effectiveness of long - duration strategies has become prominent since 2021 [27][32]. - Passive index - type bond funds have a cost - saving advantage of about 11 - 15bp per year compared with actively managed products, which is a relatively certain "hidden alpha" for investors [2][33]. 3.2 Long - Duration Index Bond Funds: The Best Combination of "High Duration" and "Low Cost" 3.2.1 Choices of Long - Duration Index Tools - Mainstream long - duration index bond funds are divided into three categories: local government bonds, Treasury bonds, and policy - financial bonds, and can be further divided into "long - term tools" (7 - 10 years and 10 years) and "ultra - long - term tools" (30 years) [3][48]. - Among them, the 10 - year index - type bond funds mainly hold bonds with a remaining term of 7 - 10 years, similar to 7 - 10 - year products. The 7 - 10 - year policy - financial bond index funds are the most popular, while the local bond index funds are scarce [48][49]. 3.2.2 Differences in the Long - Duration Index Toolbox - From the duration dimension, the 10 - year and 30 - year Treasury bonds have significant differences in risk - return characteristics, corresponding to the "ballast" and "offensive spear" roles respectively. The 10 - year Treasury bond is suitable for stable long - term investment, while the 30 - year Treasury bond is suitable for aggressive investors [54]. - From the bond type dimension, 7 - 10 - year policy - financial bonds are similar to Treasury bonds, while local bonds have unique characteristics. Although the long - term performance of 7 - 10 - year local bonds is good, investors may need more patience. The investment value of Treasury bonds and policy - financial bonds is converging [55][56][61]. 3.3 Investment Strategies for Long - Duration Index Bond Funds 3.3.1 Allocation: Enhancing Portfolio Returns, "Offensive and Defensive" - Ten - year Treasury bond ETFs are effective "offensive - and - defensive" tools for enhancing portfolio returns. In the interest - rate downward cycle, they have excellent return - capturing ability, such as the 9.02% annual return of Cathay Shanghai Stock Exchange 10 - Year Treasury Bond ETF in 2024 [7][65]. 3.3.2 Trading: Capturing Band - Trading Returns from "Point - Type Market Conditions" - Long - duration index products represented by ten - year Treasury bond ETFs have high liquidity and trading convenience, and are suitable for capturing band - trading returns from "point - type market conditions" in the bond market, such as the rapid decline and rebound of the 10 - year Treasury bond yield from November 2024 to January 2025 [7]. 3.3.3 Important Tools in Rotation Strategies - A "core - satellite" strategy is proposed, using long - duration interest - rate bond ETFs as the core "base" for pure - bond rotation, and tactically adjusting the satellite positions according to short - term market conditions. Back - testing shows that the improved rotation strategy can enhance returns and has better risk - adjusted returns [7][77][80].

Group 1: Inflation Overview - The June CPI year-on-year increased by 0.1%, higher than the expected 0% and the previous month’s -0.1% [1] - The core CPI, excluding food and energy, year-on-year rose by 0.7%, compared to the previous value of 0.6% [1] - The PPI year-on-year decreased by 3.6%, which was worse than the expected -3.3% [1] Group 2: Food Prices - Food prices fell by 0.4% month-on-month, but the decline was smaller than the average drop of 1.2% during the same period from 2021 to 2024 [2] - Fresh vegetable prices increased by 0.7% due to supply shortages caused by high temperatures and excessive rainfall, contrasting with the average decline of 4.1% in previous years [2] - In July, food prices continued to show a slight decline, with average prices of 28 monitored vegetables and 7 fruits dropping by 0.3% and 1.2% respectively [2] Group 3: Energy Prices - Oil prices rebounded, significantly reducing the drag on CPI from energy [2] - The average price of Brent crude oil increased by 9.1% month-on-month, leading to a month-on-month increase of 0.3% in transportation fuel prices after three consecutive months of decline [2][11] Group 4: Other Price Movements - The prices of platinum jewelry, rent, and medical services showed improvement, with platinum prices rising by 12.6%, marking the largest month-on-month increase in nearly a decade [3] - Medical service prices increased by 0.3% for three consecutive months, likely due to the implementation of new pricing guidelines [3] - Tourism services experienced a month-on-month price drop of 0.8% due to seasonal factors following the end of holiday periods [3] Group 5: PPI Analysis - The PPI remained at -0.4% month-on-month for the fourth consecutive month, indicating persistent weakness in industrial prices [4] - Seasonal and structural factors contributed to the decline, with significant price drops in the mining and raw materials sectors [4] - The increase in renewable energy generation has exerted structural pressure on traditional energy prices, contributing to the PPI decline [4] Group 6: Industry Insights - Prices in the photovoltaic and lithium battery sectors fell in June, but a policy shift is expected to curb price wars and lead to a potential rebound [5] - The automotive industry has shown signs of recovery, with prices increasing by 0.2% month-on-month after a period of aggressive price competition [6] - Overall, the CPI showed slight improvement while the PPI remained weak, indicating ongoing price stabilization efforts in various sectors [6]

证券研究报告|行业研究报告 [Table_Date] 2025 年 7 月 9 日 [Table_Title] 艾芬豪 2025Q2 铜产量同比增长 11%至 11.20 万 吨，锌产量达到 4.18 万吨 [Table_Title2] 有色金属-海外季报 [Table_Summary] 季报重点内容: 评级及分析师信息 [Table_IndustryRank] 行业评级：推荐 [Table_Pic] 行业走势图 [Table_Author] 分析师：晏溶 邮箱：yanrong@hx168.com.cn SACNO：S1120519100004 相关研究： 1.《行业点评|艾芬豪 2025Q1 铜产量同比增长 54.58%至 13.3 万吨，锌产量达到 4.27 万吨》 2025.4.9 2.《行业点评|艾芬豪矿业公布 Kamoa-Kakula ► 生产经营情况 1）铜 2025Q2，1、2、3 期选矿厂共研磨了 362 万吨矿石，生产了 112,009 吨铜，同比增长 11%。2025 年上半年的铜产量共计 245,127 吨。 6 月份，Kamoa-Kakula 一期、二期和三期选矿厂共生产铜 28,14 ...

证券研究报告|宏观点评报告 [Table_Date] 2025 年 07 月 08 日 [Table_Title] 资产配置日报：挑战 3500 复盘与思考: 7 月 8 日，科技与"反内卷"成为股市上涨的两股重要推力，盘中上证指数接近 3500 点；风险偏好回暖与 资金略微收紧的双重压力下，债市各期限收益率普遍上行。 国内资产方面，股市，大盘受红利概念拖累，涨幅适中，上证指数、沪深 300 上涨 0.70%、0.84%，中证红 利指数小幅下跌 0.05%；科技、光伏板块齐发力，双创表现较为亮眼，科创 50、创业板指上涨 1.40%、2.39%； 小微盘表现同样不弱，中证 2000、万得微盘股指上涨 1.29%、0.80%。债市，10 年、30 年国债活跃券收益率分别 上行 0.3bp、0.5bp 至 1.64%、1.86%；10 年、30 年国债期货主力合约下跌 0.08%、0.22%。 新一轮对等关税税率陆续揭晓，但资本市场暂不急于对此定价。7 月 8 日凌晨，美方陆续公布了包括日本、 韩国、老挝、缅甸在内等 14 个国家的对等关税新税率，从结果来看，其中 8 个国家税率下调，4 个国家税率维 持原状，2 ...