Investment Rating - The report provides a positive investment outlook for the AI sector, highlighting significant growth potential and commercial viability of AI applications and products. Core Insights - The AI industry is experiencing rapid growth, with domestic AI product access rates outpacing global counterparts. Notably, the revenue share of AI in some listed companies has increased to 10-30% by mid-2025 [3][42]. - Major players in the AI market are focusing on commercializing their products, with a notable increase in bidding for large AI models, indicating a strong demand for AI technology in various sectors [3][42]. - The report emphasizes the importance of user engagement and product stickiness, suggesting that products with strong user bases and integration into daily workflows are less likely to be replaced by emerging AI models [3][42]. Summary by Sections 1. Investment Logic - The report discusses the ongoing recruitment of AI talent by major domestic companies, which is expected to enhance the commercialization of AI products. The growth in AI product access rates is significant, with domestic AI products showing a month-on-month increase of 11.9% compared to a global increase of 3.5% [3][8]. - By mid-2025, some computer companies have seen their AI revenue share rise to between 10-30% [3][42]. 2. AI Product User Engagement - The top 20 AI products globally are dominated by leading internet companies and AI model developers, with ChatGPT consistently ranking first in user access [8][10]. - The report highlights that the competitive landscape for AI products is intensifying, particularly among mid-tier applications, while top-tier products maintain a stable market position [10][19]. 3. AI Product Monetization - The report identifies that the top AI products by annual recurring revenue (ARR) are primarily from leading tech companies, with ChatGPT leading at $14.279 billion, followed by Claude at $5 billion [35][38]. - In the domestic market, the top AI products also show strong revenue performance, with PictureThis leading at $143 million [38][39]. 4. Domestic AI Model Bidding Demand - The report notes a significant increase in the number of domestic AI model bidding projects, with a year-on-year growth rate of 1190% in January 2025, indicating a rapid acceptance and implementation of AI technologies in the market [42][43].

Report Industry Investment Rating - No relevant content provided Core Viewpoints - As of September 22, 2025, the valuation yields and spreads of private enterprise industrial bonds and real estate bonds in the outstanding credit bonds are generally higher than those of other varieties. Compared with last week, the yields of most varieties in non - financial and non - real estate industrial bonds have increased, while the adjustment range of real estate bond yields is relatively small. In financial bonds, the yields of medium - term varieties from 1 - 3 years have mostly declined [2][3][8] - In public urban investment bonds, the weighted average valuation yields in Jiangsu and Zhejiang are below 2.7%, and the yields of urban investment bonds in prefecture - level and district - county levels in Guizhou exceed 4.5%. In private urban investment bonds, the weighted average valuation yields in coastal provinces such as Shanghai, Zhejiang, Guangdong, and Fujian are below 3%, and the yields of varieties in prefecture - level cities in Guizhou and Yunnan are higher than 4% [2][14][22] Summary by Directory Chart 1: Outstanding Credit Bond Weighted Average Valuation Yield - Displays the weighted average valuation yields of various types of outstanding credit bonds as of September 22, including urban investment bonds, non - financial non - real estate industrial bonds (state - owned and private enterprises), real estate bonds (state - owned and private enterprises), financial bonds, etc. [10] Chart 2: Outstanding Credit Bond Weighted Average Spread - Presents the weighted average spreads of various types of outstanding credit bonds as of September 22, with the calculation benchmark being the same - term China Development Bank bonds [11] Chart 3: Change in Outstanding Credit Bond Weighted Average Valuation Yield Compared to Last Week - Shows the changes in the weighted average valuation yields of various types of outstanding credit bonds as of September 22 compared to last week, calculated based on the yields of September 22 and September 15 [12] Chart 4: Change in Outstanding Credit Bond Weighted Average Spread Compared to Last Week - Illustrates the changes in the weighted average spreads of various types of outstanding credit bonds as of September 22 compared to last week, calculated based on the spreads of September 22 and September 15 [13] Chart 5: Public Urban Investment Bond Weighted Average Valuation Yield - Details the weighted average valuation yields of public urban investment bonds in different administrative levels and regions as of September 22, such as provincial, prefecture - level, and district - county levels in various provinces [15] Chart 6: Public Urban Investment Bond Weighted Average Spread - Displays the weighted average spreads of public urban investment bonds in different administrative levels and regions as of September 22 [17] Chart 7: Change in Public Urban Investment Bond Weighted Average Valuation Yield Compared to Last Week - Shows the changes in the weighted average valuation yields of public urban investment bonds in different administrative levels and regions as of September 22 compared to last week [19] Chart 8: Private Urban Investment Bond Weighted Average Valuation Yield - Presents the weighted average valuation yields of private urban investment bonds in different administrative levels and regions as of September 22 [23] Chart 9: Private Urban Investment Bond Weighted Average Spread - Displays the weighted average spreads of private urban investment bonds in different administrative levels and regions as of September 22 [25]

- The report discusses the construction of forward and reverse arbitrage strategies in stock index futures markets. Forward arbitrage involves selling futures contracts and buying spot when the spot is undervalued and futures are overvalued, while reverse arbitrage involves buying futures contracts and selling spot when the spot is overvalued and futures are undervalued[45][46] - The formulas for calculating the arbitrage returns are provided. For forward arbitrage, the formula is: $$ P = \frac{(F_t - S_t) - (S_t + F_t M_t)(1 + r_t)^{\frac{T-t}{360}} - S_t C_s - F_t C_f}{S_t + F_t M_t} $$ For reverse arbitrage, the formula is: $$ P = \frac{(S_t - F_t) - (S_t M_l + F_t M_f)(1 + r_f)^{\frac{T-t}{360}} - S_t C_s - F_t C_f - S_t r^{\frac{T-t}{360}}}{S_t M_l + F_t M_f} $$[46] - The report evaluates the risks associated with the arbitrage process, including margin call risk, basis non-convergence risk, dividend risk, tracking error risk, and liquidity risk[46] - The report also discusses the method for predicting dividend points, which can affect the basis rate. The prediction is based on historical dividend patterns and uses the formula: $$ \text{Dividend Points} = \sum \left( \frac{\text{Per Share Dividend} \times \text{Index Closing Price} \times \text{Component Stock Weight}}{\text{Component Stock Closing Price}} \right) $$[47][50] - The report provides specific values for the annualized basis rates of the main contracts for IF, IC, IM, and IH, which are -4.66%, -12.51%, -14.77%, and -0.06% respectively[11] - The cross-period spread rates for the main contracts of IF, IC, IM, and IH are at the 78.30%, 73.30%, 84.20%, and 63.90% percentiles respectively since 2019[11] - The report includes a summary of market and industry investment consensus and differences from sell-side strategy teams, highlighting that 8 brokerages believe in enhanced policy easing expectations, 7 believe in active market liquidity, and 6 believe in a significant increase in market risk appetite[38][40]

Investment Rating - The report initiates coverage with a "Buy" rating and sets a target price of 103.2 CNY based on a 65x PE for 2026 [5]. Core Views - The company is positioned as a comprehensive solution provider in environmental regulation, with a strong focus on data services driving performance growth. The data services segment is expected to be a major growth driver from 2025 to 2027, with projected revenues of 27 billion, 46 billion, and 64 billion CNY, reflecting year-on-year growth rates of 77%, 67%, and 39% respectively [3][5]. Summary by Sections Investment Logic - The company has 25 years of experience in environmental regulation equipment, focusing on four main scenarios: data services, industrial processes, specialized applications, and public/commercial use. The data services segment saw a 16.2% year-on-year revenue increase in the first half of 2025, with new orders up 200% from January to August, providing strong support for sustained performance [2]. Data Center Business Growth - The data center business is identified as the company's largest growth point, driven by rapid increases in global data center investments and a shift from air cooling to liquid cooling solutions. The company has established a comprehensive solution covering all aspects of temperature control for data centers, enhancing cooling performance and energy efficiency [3][4][5]. Industrial Demand - The industrial temperature control market is projected to reach 23.6 billion USD by 2025, with the public and power sectors accounting for 43% of this market. The company has extensive experience in thermal management systems across various power projects, positioning it to benefit from the growing demand in specialized industrial fields [4]. Financial Projections - The company is expected to achieve revenues of 43.9 billion, 63.8 billion, and 83.4 billion CNY from 2025 to 2027, with corresponding net profits of 2.9 billion, 4.2 billion, and 6.7 billion CNY, reflecting significant growth rates [5][7].

Report Industry Investment Rating No relevant content provided. Core Viewpoints - Last week (9/15 - 9/19), bond - type ETFs had a total net capital outflow of 5.1 billion yuan, with interest - rate bond ETFs, credit - bond ETFs, and convertible - bond ETFs having net outflows of 1.9 billion yuan, 0.7 billion yuan, and 2.5 billion yuan respectively. Convertible - bond ETFs and credit - bond ETFs had significant drawdowns, while the net value of interest - rate bond ETFs changed little [2][11]. - The second batch of sci - tech bond ETFs will be listed on September 24. With the establishment of these 14 new funds, the total scale of sci - tech bond ETFs has exceeded 170 billion yuan, and the overall scale of bond ETFs has exceeded 600 billion yuan for the first time [3][14]. Summary by Directory 1. Issuance Progress Tracking - The second batch of 14 sci - tech bond ETFs from 14 public funds such as ICBC Credit Suisse Fund and Morgan Fund started issuing on September 12. They were submitted on August 20, approved on September 8, and scheduled for issuance on September 12. The total issuance scale of these 14 sci - tech bond ETFs reached 40.786 billion yuan, and 13 of them had an issuance scale of over 2.9 billion yuan [3][14]. 2. Existing Product Tracking - As of September 19, 2025, the circulating market values of interest - rate bond ETFs, credit - bond ETFs, and convertible - bond ETFs were 140 billion yuan, 355.8 billion yuan, and 70.1 billion yuan respectively, with credit - bond ETFs accounting for 63% of the scale. Compared with last week, their circulating market values decreased by 2.2 billion yuan, 0.02 billion yuan, and 3.6 billion yuan respectively [4][16]. - Among credit - bond ETFs, the circulating market values of benchmark - making credit - bond ETFs and sci - tech bond ETFs were 123.7 billion yuan and 125.9 billion yuan respectively, with a decrease of 0.6 billion yuan and an increase of 2.3 billion yuan compared to last week [19]. 3. ETF Performance Tracking - Recently, the market has shown range - bound fluctuations. In the past two weeks, the cumulative unit net values of interest - rate bond ETFs and credit - bond ETFs closed at 1.18 and 1.02 respectively [23]. - As of September 19, with February 7 as the base date, the average cumulative yield of benchmark - making credit - bond ETFs dropped to 0.30%; with July 17 as the base date, the cumulative yield of sci - tech bond ETFs dropped to - 0.46% and remained in the negative range [24]. 4. Premium/Discount Rate Tracking - Last week, the average premium/discount rates of credit - bond ETFs, interest - rate bond ETFs, and convertible - bond ETFs were - 0.17%, - 0.03%, and - 0.15% respectively, indicating that the average trading price was lower than the fund's unit net value and the allocation sentiment was low. Specifically, the weekly average premium/discount rates of benchmark - making credit - bond ETFs and sci - tech bond ETFs were - 0.23% and - 0.06% respectively [6][30]. 5. Turnover Rate Tracking - Last week, the turnover rate was in the order of interest - rate bond ETFs > convertible - bond ETFs > credit - bond ETFs. The weekly turnover rate of interest - rate bond ETFs rose to 179%, that of credit - bond ETFs remained around 89%, and that of convertible - bond ETFs dropped to 100%. Specifically, products like Huaxia Shanghai Stock Exchange Benchmark - Making Treasury Bond ETF and Haitong Shanghai Stock Exchange 5 - Year Local Government Bond ETF had relatively high turnover rates [6][36].

Macro Liquidity - The US dollar index has rebounded, and the degree of the China-US interest rate "inversion" has deepened, with inflation expectations also rising [1][14] - Offshore US dollar liquidity has generally loosened, while the domestic interbank funding situation remains balanced [1][19] Market Trading Activity - Overall market trading activity has increased, with most industry trading activities remaining above the 80th percentile [2][25] - Major indices' volatility has also risen, with the communication sector's volatility exceeding the 80th historical percentile [2][31] - Market liquidity indicators have declined, with all sectors' liquidity indicators below the 40th historical percentile [2][36] Institutional Research - The electronic, pharmaceutical, communication, non-ferrous metals, and automotive sectors have seen high research activity, while sectors like steel, electricity, utilities, machinery, light industry, building materials, and real estate have shown a rising trend in research activity [3][43] Analyst Forecasts - Analysts have continued to lower the net profit forecasts for the entire A-share market for 2025/2026, with the proportion of stocks with upward revisions increasing [4][50] - The net profit forecasts for sectors such as non-bank financials, chemicals, coal, and retail have been raised for 2025/2026 [4][21] - The net profit forecast for the Shanghai Stock Exchange 50 index for 2025/2026 has been adjusted upward [4][23] Northbound Trading Activity - Northbound trading activity has increased, but there continues to be a net sell-off overall [5][31] - Based on the top 10 active stocks, the buy-sell ratio in sectors like electronics, electric new energy, and communication has risen, while it has decreased in non-bank financials, pharmaceuticals, and non-ferrous metals [5][32] Margin Financing Activity - Margin financing has reached a high point not seen since September 2024, with a net purchase of 466.70 billion yuan last week [6][35] - The main net purchases in margin financing were in the electronic, non-bank financial, and machinery sectors, while net sales were seen in military, non-ferrous metals, and coal sectors [6][39] Active Equity Funds and ETFs - Active equity funds have increased their positions, particularly in coal, communication, and home appliance sectors, while reducing positions in computers, non-bank financials, and electronics [7][45] - ETFs have continued to see net subscriptions, primarily in personal ETFs, with significant net purchases in non-bank financials, non-ferrous metals, and machinery sectors [7][52]

Quantitative Models and Construction - **Model Name**: Macro Timing Strategy **Model Construction Idea**: The model evaluates macroeconomic growth and monetary liquidity signals to determine equity allocation levels[41][42] **Model Construction Process**: 1. The model assigns signal strengths to economic growth and monetary liquidity dimensions - Economic growth signal: 100% - Monetary liquidity signal: 50% 2. Equity allocation recommendation is derived based on these signals, with September's recommended equity position at 75% 3. Historical performance: From early 2025 to date, the strategy achieved a return of 11.75%, compared to Wind All A's return of 22.98%[41][42] **Model Evaluation**: The model provides a balanced view of macroeconomic and liquidity conditions, offering actionable insights for equity allocation[41][42] - **Model Name**: Rotation Model for Small-Cap Stocks **Model Construction Idea**: The model identifies style rotation opportunities between small-cap stocks and large-cap stocks (represented by the "茅指数")[19][20][22] **Model Construction Process**: 1. Relative net value comparison: Small-cap stocks/茅指数 relative net value is compared to its 243-day moving average - If above the moving average, small-cap stocks are preferred; otherwise, 茅指数 is recommended 2. 20-day closing price slope analysis: - Positive slope indicates preference for the respective index - Current slopes: Small-cap stocks (-0.08%) vs 茅指数 (0.24%) 3. Risk control indicators: - Volatility crowding degree (-35.58%) - 10-year government bond yield (-8.12%) - Both indicators are below risk thresholds (55% and 30%, respectively)[19][20][22] **Model Evaluation**: The model effectively captures style rotation signals and provides risk control measures for small-cap stock investments[19][20][22] --- Quantitative Factors and Construction - **Factor Name**: Stock Selection Factors **Factor Construction Idea**: Eight major stock selection factors are tracked across different stock pools (All A-shares, CSI 300, CSI 500, CSI 1000)[45][53][55] **Factor Construction Process**: 1. Factors include: - **Value**: Metrics like SP_TTM (past 12-month revenue/latest market value) - **Growth**: Metrics like OperatingIncome_SQ_Chg1Y (quarterly operating income YoY growth) - **Quality**: Metrics like ROE_FTTM (future 12-month expected net profit/shareholder equity average) - **Technical**: Metrics like Skewness_240D (240-day return skewness) - **Volatility**: Metrics like IV_CAPM (CAPM residual volatility)[53][55] 2. Weekly tracking of IC mean values and multi-long-short portfolio returns - Quality factors performed well last week, while others showed mixed results across stock pools[45][53][55] **Factor Evaluation**: Provides comprehensive insights into factor performance across different market segments, aiding in stock selection[45][53][55] - **Factor Name**: Convertible Bond Selection Factors **Factor Construction Idea**: Convertible bond factors are derived from the relationship between convertible bonds and their underlying stocks[50][53] **Factor Construction Process**: 1. Key factors include: - **Stock Consensus Expectation**: Predictive metrics for underlying stocks - **Stock Financial Quality**: Metrics like ROE_FTTM - **Convertible Bond Valuation**: Metrics like parity and bottom price premium rate[50][53] 2. Weekly tracking of IC mean values and multi-long-short portfolio returns - Positive IC mean values observed for stock consensus expectation, financial quality, stock value, and convertible bond valuation factors[50][53] **Factor Evaluation**: Offers robust predictive insights for convertible bond selection based on stock-related metrics[50][53] --- Backtesting Results Models - **Macro Timing Strategy**: - Return: 11.75% (2025 YTD) - Benchmark (Wind All A): 22.98%[41][42] - **Rotation Model for Small-Cap Stocks**: - Small-cap stocks/茅指数 relative net value: 1.88 (above 243-day moving average of 1.62) - 20-day closing price slopes: Small-cap stocks (-0.08%), 茅指数 (0.24%)[19][20][22] Factors - **Stock Selection Factors**: - IC mean values: Quality factors performed best last week[45][53][55] - **Convertible Bond Selection Factors**: - IC mean values: Positive for stock consensus expectation, financial quality, stock value, and convertible bond valuation factors[50][53]

Group 1: Reform Overview - The State Council has approved a pilot program for market-oriented allocation of factors in 10 regions, focusing on six key elements: technology, land, human resources, data, capital, and environmental resources[2] - The reform plans are tailored to local conditions, addressing critical issues in factor marketization, including land index marketization and rural homestead reform[4] Group 2: Land and Resource Allocation - The reform emphasizes market-oriented allocation of land indicators, allowing regions with surplus indicators to transfer them to areas with higher development potential, enhancing land resource efficiency[5] - Zhengzhou's plan focuses on transforming underutilized industrial land into new industrial land, supporting industrial upgrades[5] Group 3: Public Services and Population Management - The pilot regions, except Beijing, propose a system where basic public services are provided based on the place of residence, aiming for equalization of services[6] - Chengdu's plan explores matching new construction land with population trends, while other regions emphasize linking fiscal transfers and public service investments to urbanization of rural populations[6] Group 4: Rural Land Reform - The reform aims to facilitate urbanization of rural populations by exploring voluntary and compensated exit mechanisms for rural homestead rights, enhancing financial support for migrants[7] - The focus is on increasing the property value of homestead rights as urbanization progresses, thereby boosting rural residents' income potential[7] Group 5: Income Distribution and Labor Compensation - The reform plans aim to increase labor compensation in the initial distribution of income and enhance residents' income through land and capital rights[8] - Hefei's plan includes raising minimum wage standards and improving wage negotiation systems to benefit frontline workers[8] Group 6: Technology and Innovation - The plans propose granting researchers ownership or long-term usage rights of their scientific achievements, with Hefei suggesting at least 70% ownership rights for researchers[9] - There is a focus on promoting technology capitalization, including knowledge property financing and encouraging quality tech companies to go public[9] Group 7: Financial Sector Reforms - The Fuzhou-Xiamen-Quanzhou region's plan supports Taiwanese financial institutions' participation in mainland financial markets, enhancing cross-border financial cooperation[13] - The Chongqing plan encourages exploration of financial product and capital connectivity between China and Singapore[13] Group 8: Data Management and Utilization - The reform emphasizes the opening of high-value public data sets in various sectors, including health and transportation, to promote transparency and innovation[15] - The Suzhou-Nanjing region's plan explores market-oriented pricing and management of data assets, facilitating data trading[15] Group 9: Risk Considerations - There are risks related to misinterpretation of policies and potential delays in land reform progress, which may affect the overall effectiveness of the pilot programs[16]

1. Report Industry Investment Rating No information provided in the content. 2. Core Viewpoints of the Report - This week (2025/09/15 - 2025/09/19), the weighted index of REITs decreased by 0.14% to 100.42 points. The performance of major asset classes from high to low was: crude oil > pure bonds > gold > REITs > stocks > convertible bonds. [2] - In terms of the nature of underlying asset projects, property - type REITs rose 0.13% to 113.97, while concession - type REITs fell 0.46% to 84.48. From the perspective of industry types, the weekly performance from high to low was: data centers > warehousing and logistics > industrial parks > consumer - type > energy - type > rental housing for affordable housing > highways > ecological and environmental protection. [2] 3. Summary According to Relevant Catalogs 3.1 Secondary Market Price - Volume Performance - **Overall Market Performance**: The weighted index of REITs decreased by 0.14% this week. The performance of major asset classes varied, with crude oil having the highest return at 2.09% and convertible bonds having the lowest return at - 1.55%. [2] - **Performance by Project Nature**: Property - type REITs rose 0.13%, and concession - type REITs fell 0.46%. [2] - **Performance by Industry Type**: Data centers had the highest return of 1.32%, while ecological and environmental protection had the lowest return of - 2.00%. [2] - **Top - Performing REITs**: In property - type REITs, the top five in terms of increase were Huaxia Fund China Resources Youchao REIT (2.20%), CICC Yizhuang Industrial Park REIT (1.54%), Huaxia Joy City Commercial REIT (1.43%), Southern Runze Technology Data Center REIT (1.41%), and AVIC Yishang Warehousing and Logistics REIT (1.37%). In concession - type REITs, the top five were China Merchants Expressway REIT (1.89%), E Fund Shenzhen Expressway REIT (1.57%), Ping An Ningbo Jiaotou REIT (1.40%), AVIC Jingneng Photovoltaic REIT (1.15%), and Yin Hua Shaoxing Raw Water and Water Conservancy REIT (0.87%). [3] - **Turnover Rate**: Among property - type REITs, CICC Vipshop Outlets REIT, Hua'an Waigaoqiao REIT, Southern Wanguo Data Center REIT, CICC Hubei KeTou Optics Valley REIT, and China Merchants Fund Shekou Rental Housing REIT had relatively high turnover rates. Among concession - type REITs, Huatai Jiangsu Jiaokong REIT, Guojin China Railway Construction REIT, Fuguo First - Created Water Service REIT, Huaxia Huadian Clean Energy REIT, and Huaxia Tebian Electric New Energy REIT had relatively high turnover rates. [3] 3.2 Secondary Market Valuation Situation - **Property - type REITs**: The top three in terms of internal rate of return (IRR) were CICC Hubei KeTou Optics Valley REIT (7.70%), Boshi Shekou Industrial Park REIT (6.69%), and Huaxia HeDa High - tech REIT (6.61%). The three REITs with relatively low P/NAV valuation quantiles and showing undervaluation were E Fund Guangkai Industrial Park REIT, CICC China Green Development Commercial REIT, and Huitianfu Shanghai Real Estate Rental Housing REIT. [4][23] - **Concession - type REITs**: The top three in terms of IRR were Huaxia China Communications Construction REIT (9.65%), Ping An Guangzhou Guanghe REIT (8.96%), and ICBC Hebei Expressway REIT (6.18%). The three REITs with relatively low P/NAV valuation quantiles and showing undervaluation were Huaxia Yuexiu REIT, Huaxia Huadian Clean Energy REIT, and ICBC Mengneng Clean Energy REIT. [4] 3.3 Market Correlation Statistics - **Correlation between REITs and Major Asset Classes**: This week, REITs had the highest correlation coefficient with the Shanghai Composite Index at 0.20, followed by CSI 300 at 0.18, ChiNext Index at 0.11, small - and - medium - cap stocks at 0.16, CSI Convertible Bond Index at 0.17, CSI All - Bond Index at 0.07, gold at 0.04, and crude oil index at 0.09. [27] - **Correlation of Different Types of REITs with Major Asset Classes**: Different types of REITs had different correlations with major asset classes. For example, industrial park - type REITs had a relatively high correlation with the Shanghai Composite Index at 0.21, while rental housing for affordable housing - type REITs had a correlation coefficient of 0.00 with the Shanghai Composite Index. [28] 3.4 Primary Market Tracking As of September 19, 2025, there were 11 REITs products still in the exchange acceptance stage and 1 REIT in the state of having passed the review and waiting for listing. [5][31]

1. Report Industry Investment Rating - No relevant content provided 2. Core Viewpoints - The report tracks the supply and trading of local government bonds, including the issuance rhythm, pricing, and secondary - market trading characteristics [2][3] 3. Summary by Directory 3.1 First - level Supply Rhythm - From September 8 to September 12, 2025, local government bonds worth 301.7 billion yuan were issued, including 131.9 billion yuan of new special bonds and 68 billion yuan of refinancing special bonds [2][9] - As of September 12, 2025, 41.4 billion yuan of special refinancing special bonds were issued in September, accounting for 6.8% of the monthly local bond issuance scale [2][9] - The average issuance interest rate of local bonds continued to rise. The spreads between the issuance interest rates of 30 - year, 20 - year, and 10 - year local bonds and the same - term treasury bonds widened to 19BP, 22BP, and 20BP respectively [2][16] - In September, Guangdong, Guizhou, Guangxi, Hebei, Sichuan, Hunan and other provinces were the main regions for local bond issuance. The issuance scale of 20 - 30 - year local bonds in Guangdong was close to 60 billion yuan, and the average coupon rates of local government bonds in Hunan, Guangxi, and Jilin were above 2.3% [18] 3.2 Second - level Trading Characteristics - Last week, the weekly fluctuations of 7 - 10 - year and over - 10 - year local bond indices were - 0.41% and - 0.97% respectively. The decline was smaller than that of over - 10 - year treasury bonds and almost the same as that of ultra - long - term credit bonds [3][23] - In terms of provinces, the trading activity of Guangdong government bonds increased, with the weekly trading volume increasing by 127 transactions compared with the previous period. The trading volumes of local bonds in Anhui and Jiangsu decreased significantly [3][23] - In terms of trading returns, the average trading term of Guangdong government bonds was about 27 years, with an average trading return of about 2.31%. The average trading terms of Sichuan and Jiangxi government bonds were close to 25.5 years, and the average trading returns were basically between 2.2% and 2.3% [3][23]