Investment Rating - The report maintains a positive outlook on the basic chemical industry [6] Core Views - The agricultural chemical sector is experiencing a recovery in market conditions, with prices for glyphosate and paraquat increasing. Glyphosate prices reached 25,501 CNY/ton, up 200 CNY/ton from the previous week, with a gross profit of 2,772.4 CNY/ton, an increase of 132.2 CNY/ton [5][20] - The demand for agricultural chemicals is expected to remain strong, particularly if major players like Monsanto exit the market, which could lead to supply shocks for glyphosate and related products [5] - The fluorochemical sector is seeing price increases for R32 refrigerant due to seasonal demand and supply constraints, with R32 priced at 53,500 CNY/ton, up 500 CNY/ton from the previous week [4][31] Summary by Sections (1) Key Chemical Product Price Tracking - Prices for key monitored products such as vitamin B1 (up 12.82%) and vitamin D3 (up 7.14%) saw significant weekly increases, while hydrochloric acid (-8.29%) and isobutylene (-7.98%) experienced notable declines [15] (2) Polyurethane: MDI Prices Down, TDI Prices Up - MDI prices decreased due to weak domestic demand and external market conditions, with average prices for polymer MDI at 14,850 CNY/ton (down 0.67%) and pure MDI at 16,450 CNY/ton (down 0.9%). In contrast, TDI prices increased to 12,650 CNY/ton (up 2.85%) [18] (3) Agricultural Chemicals: Glyphosate, Urea, and Potassium Chloride Prices Up - Glyphosate prices increased to 25,501 CNY/ton, with a weekly production of 11,000 tons and a decrease in inventory to 34,100 tons. Urea and potassium chloride also saw price increases, with urea priced at 1,816 CNY/ton (up 0.94%) and potassium chloride at 3,339 CNY/ton (up 3.53%) [20][27] (4) Fluorochemicals: R32 Refrigerant Price Increase - The price of R32 refrigerant rose to 53,500 CNY/ton due to increased demand from the air conditioning sector and supply constraints. The prices of upstream materials like fluorspar and hydrofluoric acid remained stable [31]

金 金融工程点评 E-MAIL：liuxf@tpyzq.com 执业资格证书编码：S1190522090001 研究助理：孙弋轩 电话：18910596766 E-MAIL：sunyixuan@tpyzq.com [Table_Title] 金工 ETF 点评：跨境 ETF 单日净流入 20.67 亿元；电子、汽车、家电拥挤低位 [Table_Author] 证券分析师：刘晓锋 电话：13401163428 一般证券业务登记编码：S1190123080008 一、资金流向 二、行业拥挤度监测 ◼ 通过构建行业拥挤度监测模型，对申万一级行业指数的拥挤度进行每日监测， 前一交易日有色金属、钢铁拥挤度靠前，相比较而言，汽车、电子的拥挤度 水平较低，建议关注。此外，电力设备单日拥挤度变动较大。从主力资金流 动来看，前一交易日主力资金流入非银、计算机，流出电子、电力设备、传 媒。近三个交易日主力资金增配非银。 三、ETF 产品关注信号 ◼ 根据溢价率 Z-score 模型搭建相关 ETF 产品筛选信号模型，通过滚动测算提 供存在潜在套利机会的标的，此外需警惕标的回调风险。 风险提示：本报告结论完全基于公开历史数据，建议 ...

电力设备与新能源行业 证券研究报告 |报告类型 2025/07/13 云南省清洁能源深度（一）—— 资源赋能水电大省，国际清洁能源枢纽 证券分析师: 刘强 分析师登记编号: S1190522080001 证券分析师: 钟欣材 分析师登记编号: S1190524110004 P2 报告摘要 1、自然资源充沛，水风光具有优越的发展条件，光伏装机增速亮眼 云南省水能资源丰富，省内河流众多，全省水资源总量2141亿立方米（多年平均值），居全国第3位。太阳能资源较丰 富，仅次于西藏、青海、内蒙古等省。充沛的自然资源为云南省清洁能源发展提供了优越的条件。截止2024年末，云南 省水电、光伏累计装机量达83.60、39.16GW，水电装机稳步增长，光伏装机增速亮眼。 2、时空互补保障消纳，消纳能力提升有望进一步打开清洁能源装机空间 云南省重视消纳保障，积极推进新型储能示范性项目建设、抽蓄项目核准。截止2025年5月13日，云南核准抽水蓄能电 站4座，总计装机容量约5.9GW，为消纳提供强有力保障。同时电站开发环节，积极推挤水风光多能互补，平滑日度、季 度发电曲线。 3、持续疏通西电东送+跨境输送，构建国际清洁能源枢纽 20 ...

2025-07-14 行业周报 看好/维持 航空航天与国防Ⅲ 工业资本货物 军工行业周报(2025.07.07-2025.07.13)：我国天然铀生产迎来新突破 走势比较 -20% -10% 0% 10% 20% 30% 40% 50% 60% Jul/24 Aug/24 Sep/24 Oct/24 Nov/24 Dec/24 Jan/25 Feb/25 Mar/25 Apr/25 May/25 Jun/25 航空航天与国防 沪深300 推荐公司及评级 相关研究报告 证券分析师：马浩然 电话：010-88321893 E-MAIL：mahr@tpyzq.com 执业资格证书编码：S1190517120003 【太平洋证券】国防军工 2025 年度 策略：聚焦新域新质，迎接景气拐点 报告摘要 本周要闻： 我国天然铀生产迎来新突破 据央视新闻 7 月 12 日报道，从中核集团获悉，我国规模最大的 天然铀产能项目"国铀一号"示范工程成功生产下线"第一桶铀"， 标志着我国天然铀生产取得突破，将有力保障我国能源资源安全的自 主可控。"国铀一号"示范工程是我国产能规模最大、建设标准最 高、建设速度最快的天然铀生产基地，位 ...

- The report constructs an industry crowding model to monitor the daily crowding levels of Shenwan primary industry indices, identifying high crowding in building materials and electrical equipment, while home appliances and transportation show lower levels[3] - A Z-score model is used to screen ETF products based on premium rates, providing signals for potential arbitrage opportunities and warning of potential risks of price corrections[4] - The industry crowding model highlights significant daily changes in crowding levels for agriculture, forestry, animal husbandry, and fishery, as well as non-ferrous metals[3] - The Z-score model applies rolling calculations to identify ETFs with potential arbitrage opportunities, focusing on premium rate deviations[4] - The industry crowding model suggests monitoring industries with extreme crowding levels for potential investment opportunities or risks[3] - The Z-score model emphasizes the importance of tracking premium rate deviations to identify arbitrage opportunities and mitigate risks[4]

Group 1 - The core viewpoint indicates that domestic corporate profits remain under pressure, with capital and risk appetite driving the A-share market's upward fluctuations. The financial sectors such as banks, non-banking financial institutions, pharmaceuticals, and telecommunications are expected to lead this trend, with an anticipated increase in risk appetite by late July [3][4][12]. - As of May, the cumulative profit of industrial enterprises above designated size turned negative year-on-year, and the manufacturing PMI for June was at 49.7, indicating marginal improvement but still below the growth line. Only six industries have seen upward adjustments in profit expectations for 2025, including steel, social services, and media, suggesting that corporate profit growth remains in a bottoming phase [4][12][17]. - The overall profitability indicators, ROA and ROE, remain weak, with banks, steel, and transportation showing relatively better performance [4][12]. Group 2 - Micro liquidity is showing a net inflow trend, with equity mutual funds issuing 272.6 billion units since the beginning of the year, and the margin trading scale has continued to see net inflows since May. Northbound capital saw a significant increase in Q2, with a net inflow of 61.7 billion, compared to 13.5 billion in Q1, particularly in sectors like power equipment, pharmaceuticals, and telecommunications [5][13]. - The issuance of special government bonds and the recent political meetings are expected to enhance market risk appetite. The path from special bonds to bank capital supplementation and interest rate cuts is clear, benefiting overall macro liquidity [6][14]. Group 3 - The investment strategy recommends three main lines: first, sectors like banks and public utilities that represent bond-like characteristics due to weak profits and strong liquidity; second, sectors such as photovoltaics, live pigs, and glass that are expected to benefit from policy negotiations and rising risk appetite; third, sectors like pharmaceuticals and telecommunications that will benefit from incremental capital inflows [7][16]. - The report anticipates that the trade war is likely to settle in the third quarter, with the narrative of "American exceptionalism" potentially returning to market focus, leading to a resurgence of the dollar and U.S. stocks [7][41].

Group 1: CPI Analysis - China's June CPI increased by 0.1% year-on-year, reversing a four-month decline, exceeding market expectations[4] - The month-on-month CPI decreased by 0.1%, but the decline was narrower than the previous month by 0.1 percentage points[6] - Industrial consumer goods prices stabilized and contributed to the CPI increase, with a reduction in the downward impact on CPI by approximately 0.18 percentage points[6] Group 2: PPI Analysis - China's June PPI decreased by 3.6% year-on-year, with the decline widening by 0.3 percentage points compared to the previous month[4] - The PPI's further decline reflects accumulated supply-side pressures and weak demand[23] - Upstream industrial prices faced significant pressure, with mining industry prices dropping by 13.2% year-on-year[27] Group 3: Price Trends - Core CPI, excluding food and energy, rose by 0.7%, marking a 14-month high, supported by resilient service prices[20] - Energy prices increased by 0.1% month-on-month, reversing the previous downward trend, influenced by geopolitical factors[16] - Food prices decreased by 0.4% month-on-month, but the decline was smaller than seasonal expectations by 0.5 percentage points[14]

- The industry crowding monitoring model was constructed to monitor the daily crowding levels of Shenwan primary industry indices. It identified utilities and building materials as having high crowding levels, while automotive, food & beverage, and home appliances showed low crowding levels. The model also tracked significant daily changes in crowding levels for industries like agriculture, coal, and environmental protection[4] - The Z-score premium rate model was developed to screen ETF products for potential arbitrage opportunities. This model uses rolling calculations to identify signals and warns of potential risks of price corrections for the identified ETFs[5] - Daily net inflows for broad-based ETFs amounted to 3.77 billion yuan, with top inflows observed in CSI 1000 ETF (+7.78 billion yuan), SSE 50 ETF (+6.96 billion yuan), and CSI 300 ETF (+5.38 billion yuan). Conversely, top outflows were recorded for ChiNext ETF (-6.73 billion yuan), CSI A500 ETF (-4.06 billion yuan), and STAR 50 ETF (-3.51 billion yuan)[6] - Industry-themed ETFs saw a daily net inflow of 1.82 billion yuan, with top inflows in Military ETF (+4.01 billion yuan), Securities ETF (+2.63 billion yuan), and Defense ETF (+2.31 billion yuan). Top outflows were noted for Robotics ETF (-1.39 billion yuan), Semiconductor ETF (-1.05 billion yuan), and AI ETF (-0.99 billion yuan)[6] - Style-strategy ETFs recorded a daily net inflow of 2.29 billion yuan, with top inflows in Low Volatility Dividend ETF (+1.62 billion yuan), Low Volatility Dividend 50 ETF (+0.53 billion yuan), and Dividend State-Owned Enterprise ETF (+0.28 billion yuan). Top outflows included CSI Dividend ETF (-0.19 billion yuan), Low Volatility Dividend ETF (-0.18 billion yuan), and Low Volatility Dividend 100 ETF (-0.15 billion yuan)[6] - Cross-border ETFs experienced a daily net outflow of 0.51 billion yuan, with top inflows in Hong Kong Non-Bank ETF (+3.84 billion yuan), Hang Seng Low Volatility Dividend ETF (+0.63 billion yuan), and S&P 500 ETF (+0.42 billion yuan). Top outflows were observed for Hang Seng Tech ETF (-1.19 billion yuan), Hong Kong Dividend ETF (-0.82 billion yuan), and Nasdaq 100 ETF (-0.69 billion yuan)[6]

Investment Rating - The industry rating is optimistic, expecting overall returns to exceed the CSI 300 index by more than 5% in the next six months [10]. Core Insights - The report highlights a recent land auction in Suzhou, where three residential plots were sold for a total of 1.548 billion yuan, with all plots sold at the starting price [5]. - The report notes that the real estate sector has seen a slight decline in stock performance, with the Shanghai Composite Index and Shenzhen Composite Index down by 0.13% and 0.04% respectively on July 9, 2025 [3]. - The report indicates that the real estate sector's performance is mixed, with some stocks showing significant gains while others have experienced declines [4]. Market Trends - On July 9, 2025, the overall market saw declines, with the Shenwan Real Estate Index down by 0.01% [3]. - The report lists the top five gainers in the real estate sector, with Yucheng Development leading at a 10.05% increase, while the largest decliners included Nanshan Holdings with a decrease of 2.70% [4]. Company Announcements - Shanghai Jinmao Investment announced that "22 Jinmao 03" will be delisted early on July 18, 2025 [8]. - Financial Street reported the resignation of a board member due to personal reasons [8]. - Jianfa Real Estate Group has initiated the issuance of corporate bonds aimed at professional investors, with a total issuance scale not exceeding 1.75 billion yuan [8].

- The report mentions the construction of an "industry crowding monitoring model" to track the crowding levels of Shenwan first-level industry indices on a daily basis. The model identifies industries with high crowding levels, such as utilities and building materials, and those with lower levels, like automobiles and food & beverage. It also highlights significant daily changes in crowding levels for industries like real estate and utilities[6] - Another model mentioned is the "premium rate Z-score model," which is used to screen ETF products for potential arbitrage opportunities. The model employs rolling calculations to identify ETFs with potential risks of price corrections[6] - The industry crowding monitoring model evaluates crowding levels based on daily fund flows and crowding metrics, providing insights into industry trends and fund allocation changes over recent trading days[6] - The premium rate Z-score model calculates Z-scores for ETF premium rates, identifying deviations from historical averages that may signal arbitrage opportunities or risks[6] - The industry crowding monitoring model is qualitatively assessed as effective for identifying industry trends and fund allocation shifts, aiding investors in decision-making[6] - The premium rate Z-score model is qualitatively evaluated as useful for detecting arbitrage opportunities and potential risks in ETF pricing[6] - The industry crowding monitoring model highlights utilities and building materials as having high crowding levels, while automobiles and food & beverage exhibit lower levels. Real estate and utilities show significant daily crowding level changes[6] - The premium rate Z-score model identifies ETFs with potential arbitrage opportunities based on deviations in premium rates, though specific Z-score values are not provided in the report[6]