Quantitative Models and Construction Methods Excellent Fund Performance Enhancement Portfolio - Model Name: Excellent Fund Performance Enhancement Portfolio - Model Construction Idea: The model aims to benchmark against active equity funds instead of broad-based indices, leveraging quantitative methods to enhance the selection of top-performing funds[4][19][50] - Model Construction Process: - Benchmark against active equity funds' median returns, using the equity hybrid fund index (885001.WI) as a proxy - Utilize quantitative methods to enhance the selection based on the holdings of top-performing funds - Consider fund performance factors and neutralize them to avoid style concentration issues - Optimize the portfolio to control deviations in individual stocks, industry, and style from the selected fund holdings[4][19][50] - Model Evaluation: The model shows good stability and can consistently outperform the median of active equity funds[50] - Model Testing Results: - Annualized return of 20.31% from 2012.1.4 to 2025.6.30, with an annualized excess return of 11.83% compared to the equity hybrid fund index[51][54] Exceeding Expectations Selection Portfolio - Model Name: Exceeding Expectations Selection Portfolio - Model Construction Idea: The model focuses on stocks with significant positive earnings surprises, selecting those with both fundamental support and technical resonance[5][24][55] - Model Construction Process: - Screen stocks based on research report titles indicating earnings surprises and analysts' upward revisions of net profit - Perform dual-layer selection on the stock pool based on fundamental and technical aspects - Construct a portfolio of stocks that meet both fundamental and technical criteria[5][24][55] - Model Evaluation: The model can consistently rank in the top 30% of active equity funds each year[55] - Model Testing Results: - Annualized return of 30.55% from 2010.1.4 to 2025.6.30, with an annualized excess return of 24.68% compared to the equity hybrid fund index[56][58] Broker Golden Stock Performance Enhancement Portfolio - Model Name: Broker Golden Stock Performance Enhancement Portfolio - Model Construction Idea: The model leverages the stock pool of broker golden stocks, optimizing the portfolio to control deviations from the stock pool in terms of individual stocks, industry, and style[6][32][60] - Model Construction Process: - Use the broker golden stock pool as the selection space and constraint benchmark - Optimize the portfolio to control deviations from the broker golden stock pool in terms of individual stocks, industry, and style[6][32][60] - Model Evaluation: The model can consistently rank in the top 30% of active equity funds each year[60] - Model Testing Results: - Annualized return of 19.34% from 2018.1.2 to 2025.6.30, with an annualized excess return of 14.38% compared to the equity hybrid fund index[61][64] Growth and Stability Portfolio - Model Name: Growth and Stability Portfolio - Model Construction Idea: The model adopts a "time-series first, cross-sectional later" approach to construct a two-dimensional evaluation system for growth stocks, focusing on the period before the official financial report release[7][38][65] - Model Construction Process: - Screen growth stocks based on research report titles indicating earnings surprises and significant earnings growth - Prioritize stocks closer to the financial report release date, and use multi-factor scoring to select high-quality stocks when the sample size is large - Introduce mechanisms to reduce portfolio turnover and avoid risks, such as weak balance, transition, buffer, and risk avoidance mechanisms[7][38][65] - Model Evaluation: The model can consistently rank in the top 30% of active equity funds each year[65] - Model Testing Results: - Annualized return of 35.51% from 2012.1.4 to 2025.6.30, with an annualized excess return of 26.88% compared to the equity hybrid fund index[66][69] Model Backtesting Results - Excellent Fund Performance Enhancement Portfolio: - Absolute return this week: -0.28%, annual absolute return: 27.54%, annual excess return: -3.91%[2][23] - Exceeding Expectations Selection Portfolio: - Absolute return this week: 1.29%, annual absolute return: 45.51%, annual excess return: 14.06%[2][31] - Broker Golden Stock Performance Enhancement Portfolio: - Absolute return this week: 0.39%, annual absolute return: 33.97%, annual excess return: 2.52%[2][37] - Growth and Stability Portfolio: - Absolute return this week: -1.23%, annual absolute return: 51.45%, annual excess return: 20.00%[3][44]

Quantitative Models and Construction Methods 1. Model Name: 250-Day New High Distance - **Model Construction Idea**: This model tracks the distance of a stock's closing price from its 250-day high to identify momentum and trend-following opportunities in the market. It is inspired by studies showing that stocks near their 52-week highs tend to outperform[11][18]. - **Model Construction Process**: The formula for the 250-day new high distance is: $ 250\ Day\ New\ High\ Distance = 1 - \frac{Close_t}{ts\_max(Close, 250)} $ Where: - $Close_t$ represents the latest closing price - $ts\_max(Close, 250)$ represents the maximum closing price over the past 250 trading days If the latest closing price reaches a new high, the distance is 0. If the price has fallen from the high, the distance is a positive value indicating the percentage drop[11]. - **Model Evaluation**: This model effectively captures market momentum and highlights stocks or indices that are leading the market trends[11][18]. 2. Model Name: Stable New High Stock Screening - **Model Construction Idea**: This model identifies stocks with stable price paths and consistent momentum, as smoother price trajectories are associated with stronger momentum effects[27]. - **Model Construction Process**: The screening process involves the following steps: - **Analyst Attention**: Stocks must have at least 5 "Buy" or "Overweight" ratings in the past 3 months - **Relative Strength**: Stocks must rank in the top 20% of the market based on 250-day price performance - **Price Stability**: Stocks are scored based on two metrics: - **Price Path Smoothness**: Measured by the ratio of price displacement to the total price path length - **Momentum Consistency**: Calculated as the time-series average of the 250-day new high distance over the past 120 days - **Trend Continuation**: Stocks are ranked based on the 5-day average of the 250-day new high distance, and the top 50 stocks are selected[27][29]. - **Model Evaluation**: This model emphasizes the importance of smooth and consistent price movements, which are less likely to attract excessive attention and thus generate stronger momentum effects[27][29]. --- Model Backtesting Results 1. 250-Day New High Distance - **Indices' 250-Day New High Distance**: - Shanghai Composite: 1.63% - Shenzhen Component: 1.09% - CSI 300: 1.08% - CSI 500: 1.24% - CSI 1000: 1.54% - CSI 2000: 1.91% - ChiNext Index: 1.79% - STAR 50 Index: 1.28%[2][12][34] 2. Stable New High Stock Screening - **Selected Stocks**: 50 stocks were identified, including Industrial Fulian, Giant Network, and Shengyi Electronics. - **Sector Distribution**: - Technology: 18 stocks (e.g., Electronics) - Manufacturing: 15 stocks (e.g., Machinery)[3][30][35] --- Quantitative Factors and Construction Methods 1. Factor Name: 250-Day New High Distance - **Factor Construction Idea**: Measures the relative position of a stock's closing price to its 250-day high, capturing momentum and trend-following signals[11]. - **Factor Construction Process**: $ 250\ Day\ New\ High\ Distance = 1 - \frac{Close_t}{ts\_max(Close, 250)} $ - $Close_t$: Latest closing price - $ts\_max(Close, 250)$: Maximum closing price over the past 250 trading days[11]. 2. Factor Name: Price Path Smoothness - **Factor Construction Idea**: Quantifies the smoothness of a stock's price trajectory, as smoother paths are associated with stronger momentum effects[27]. - **Factor Construction Process**: - **Price Path Smoothness**: Ratio of price displacement to total price path length over a given period[27]. 3. Factor Name: Momentum Consistency - **Factor Construction Idea**: Measures the stability of a stock's momentum over time, emphasizing consistent performance[27]. - **Factor Construction Process**: - **Momentum Consistency**: Time-series average of the 250-day new high distance over the past 120 days[27]. 4. Factor Name: Trend Continuation - **Factor Construction Idea**: Captures short-term momentum by analyzing recent price movements[27]. - **Factor Construction Process**: - **Trend Continuation**: 5-day average of the 250-day new high distance, with stocks ranked based on this metric[27]. --- Factor Backtesting Results 1. 250-Day New High Distance - **Indices' 250-Day New High Distance**: - Shanghai Composite: 1.63% - Shenzhen Component: 1.09% - CSI 300: 1.08% - CSI 500: 1.24% - CSI 1000: 1.54% - CSI 2000: 1.91% - ChiNext Index: 1.79% - STAR 50 Index: 1.28%[2][12][34] 2. Stable New High Stock Screening Factors - **Selected Stocks**: 50 stocks were identified, including Industrial Fulian, Giant Network, and Shengyi Electronics. - **Sector Distribution**: - Technology: 18 stocks (e.g., Electronics) - Manufacturing: 15 stocks (e.g., Machinery)[3][30][35]

Quantitative Models and Construction Methods 1. Model Name: 250-Day New High Distance Model - **Model Construction Idea**: This model tracks the distance of the latest closing price from the highest closing price over the past 250 trading days. It is used to identify stocks or indices that are approaching or have surpassed their historical highs, which can serve as indicators of market trends and hotspots[11][18]. - **Model Construction Process**: The formula for the 250-day new high distance is: $ 250\ Day\ New\ High\ Distance = 1 - \frac{Close_t}{ts\_max(Close, 250)} $ Where: - $ Close_t $ represents the latest closing price - $ ts\_max(Close, 250) $ represents the maximum closing price over the past 250 trading days If the latest closing price reaches a new high, the distance is 0. If the price has fallen from the high, the distance is a positive value indicating the percentage drop[11]. - **Model Evaluation**: The model is effective in identifying stocks or indices with strong momentum and can be used to monitor market trends and leading sectors[11][18]. 2. Model Name: Stable New High Stock Screening Model - **Model Construction Idea**: This model focuses on identifying stocks that not only achieve new highs but also exhibit stable price paths and consistent momentum. It incorporates factors such as analyst attention, relative strength, and price stability to refine the selection of high-momentum stocks[27][29]. - **Model Construction Process**: The screening criteria include: - **Analyst Attention**: At least 5 buy or overweight ratings in the past 3 months - **Relative Strength**: 250-day price change in the top 20% of the market - **Price Stability**: Stocks are ranked based on the following metrics: - **Price Path Smoothness**: Ratio of price displacement to the total price path - **New High Continuity**: Average 250-day new high distance over the past 120 days - **Trend Continuity**: Average 250-day new high distance over the past 5 days The top 50 stocks based on these criteria are selected as stable new high stocks[27][29]. - **Model Evaluation**: The model emphasizes the temporal characteristics of momentum and identifies stocks with smoother price paths, which are less likely to experience extreme volatility[27][29]. --- Model Backtesting Results 1. 250-Day New High Distance Model - **Indices' 250-Day New High Distance**: - Shanghai Composite: 1.63% - Shenzhen Component: 1.09% - CSI 300: 1.08% - CSI 500: 1.24% - CSI 1000: 1.54% - CSI 2000: 1.91% - ChiNext Index: 1.79% - STAR 50 Index: 1.28%[12][13][34] 2. Stable New High Stock Screening Model - **Selected Stocks**: 50 stocks were identified, including Industrial Fulian, Giant Network, and Shengyi Electronics. - **Sector Distribution**: - Technology: 18 stocks (e.g., Electronics) - Manufacturing: 15 stocks (e.g., Machinery)[30][35] --- Quantitative Factors and Construction Methods 1. Factor Name: 250-Day New High Distance - **Factor Construction Idea**: Measures the relative position of the latest closing price to the highest price in the past 250 trading days, indicating momentum strength[11]. - **Factor Construction Process**: The formula is: $ 250\ Day\ New\ High\ Distance = 1 - \frac{Close_t}{ts\_max(Close, 250)} $ Where: - $ Close_t $ is the latest closing price - $ ts\_max(Close, 250) $ is the maximum closing price over the past 250 trading days[11]. - **Factor Evaluation**: This factor effectively captures momentum and is widely used in trend-following strategies[11][18]. 2. Factor Name: Price Path Smoothness - **Factor Construction Idea**: Evaluates the stability of a stock's price movement by comparing the displacement of the price path to its total length[27]. - **Factor Construction Process**: $ Price\ Path\ Smoothness = \frac{Price\ Displacement}{Total\ Price\ Path} $ Where: - Price Displacement is the straight-line distance between the starting and ending prices - Total Price Path is the cumulative sum of absolute daily price changes over a given period[27]. - **Factor Evaluation**: Stocks with smoother price paths tend to exhibit stronger and more sustainable momentum[27]. 3. Factor Name: New High Continuity - **Factor Construction Idea**: Measures the consistency of a stock's ability to maintain new highs over time[29]. - **Factor Construction Process**: $ New\ High\ Continuity = Average\ (250\ Day\ New\ High\ Distance\ Over\ Past\ 120\ Days) $ This factor calculates the mean of the 250-day new high distance over a rolling 120-day window[29]. - **Factor Evaluation**: This factor highlights stocks with persistent upward trends, making them attractive for momentum-based strategies[29]. --- Factor Backtesting Results 1. 250-Day New High Distance - **Indices' 250-Day New High Distance**: - Shanghai Composite: 1.63% - Shenzhen Component: 1.09% - CSI 300: 1.08% - CSI 500: 1.24% - CSI 1000: 1.54% - CSI 2000: 1.91% - ChiNext Index: 1.79% - STAR 50 Index: 1.28%[12][13][34] 2. Price Path Smoothness and New High Continuity - **Selected Stocks**: 50 stocks were identified, including Industrial Fulian, Giant Network, and Shengyi Electronics. - **Sector Distribution**: - Technology: 18 stocks (e.g., Electronics) - Manufacturing: 15 stocks (e.g., Machinery)[30][35]

Report Industry Investment Rating No relevant content provided. Core View No specific core view was clearly presented in the given text. Summary by Related Content Government Bond Net Financing - Government bond net financing was 60.84 billion yuan in Week 37 (9/8 - 9/14) and 31.79 billion yuan in Week 38 (9/15 - 9/21). As of Week 37, the cumulative amount reached 1.11 trillion yuan, exceeding the same period last year by 490 billion yuan [1][7]. - The sum of national debt net financing and new local bond issuance was 56.22 billion yuan in Week 37 and 40.56 billion yuan in Week 38. As of Week 37, the cumulative general deficit was 870 billion yuan, with a progress of 78.5%, surpassing the same period last year [1][7]. National Debt - National debt net financing was 41.56 billion yuan in Week 37 and 28.71 billion yuan in Week 38. The total national debt net financing for the year is 666 billion yuan. As of Week 37, the cumulative amount was 530 billion yuan, with a progress of 78.9%, exceeding the average of the past five years [10]. Local Debt - Local debt net financing was 19.28 billion yuan in Week 37 and 3.09 billion yuan in Week 38. As of Week 37, the cumulative amount was 590 billion yuan, exceeding the same period last year by 280 billion yuan [12]. - New general debt issuance was 1.47 billion yuan in Week 37 and 2.07 billion yuan in Week 38. The local deficit for 2025 is 80 billion yuan. As of Week 37, the cumulative new general debt was 63.55 billion yuan, with a progress of 79.4%, exceeding the same period last year [12]. - New special - purpose debt issuance was 13.19 billion yuan in Week 37 and 9.78 billion yuan in Week 38. The planned new special - purpose debt for 2025 is 440 billion yuan. As of Week 37, the cumulative amount was 340 billion yuan, with a progress of 77.6%, exceeding the same period last year. Special new special - purpose debt of 118.19 billion yuan has been issued, including 21.4 billion yuan since September. Land reserve special - purpose debt of 33.02 billion yuan has been issued [2][15]. Special Refinancing Bonds - Special refinancing bond issuance was 2.62 billion yuan in Week 37 and 2.14 billion yuan in Week 38. As of Week 37, the cumulative amount was 196 billion yuan, with a issuance progress of 98% [2][30]. Urban Investment Bonds - Urban investment bond net financing was 1.55 billion yuan in Week 37 and is expected to be - 0.7 billion yuan in Week 38. As of this week, the balance of urban investment bonds is 1.02 trillion yuan [3][33].

The provided content does not contain any specific quantitative models or factors, nor does it include their construction processes, formulas, evaluations, or backtesting results. The documents primarily focus on market performance, sector analysis, ETF premiums/discounts, institutional activities, and other market-related data. There is no relevant information to summarize under the requested structure for quantitative models or factors.

Group 1: Baidu Group Analysis - Baidu Group is expected to experience a revaluation of its value due to the AI wave, with self-developed chips, AI cloud services, and AI applications driving growth [6][9] - In Q2 2025, Baidu's core advertising business accounted for approximately 50% of revenue, while AI-related businesses contributed about 30%, showing rapid growth [6][9] - The revenue from Kunlun chips is projected to reach 5 billion RMB in 2025 and 10 billion RMB in 2026, with significant demand from external clients [7] Group 2: AI Cloud and Autonomous Driving - Baidu's AI cloud revenue in Q2 2025 was 6.5 billion RMB, a year-on-year increase of 27%, with expectations to reach 27.4 billion RMB for the full year [7] - The Apollo Go service is projected to exceed 10 million orders in 2025, with a significant increase in ride services provided [8] - AI advertising and digital content generation are showing promising growth, with AI-generated content accounting for 64% of mobile search results in July 2025 [8] Group 3: Financial Forecasts - Revenue forecasts for Baidu have been adjusted upwards for 2025-2027, with expected revenues of 133.6 billion RMB, 143.7 billion RMB, and 154.1 billion RMB respectively [9] - The adjusted net profit estimates for the same period are 21 billion RMB, 24.4 billion RMB, and 28 billion RMB, reflecting a slight increase from previous estimates [9] - As of June 30, 2025, Baidu's net cash stood at 155.1 billion RMB, providing a solid foundation for future growth [9] Group 4: Sustainable Aviation Fuel (SAF) Industry - The SAF industry is experiencing growth driven by EU regulations, with a projected demand of 3.58 million tons by 2050 [11][14] - China's SAF production capacity is expected to exceed 1 million tons by the end of 2024, with significant potential for growth [14] - The price of high-end SAF has increased by 55% since the beginning of the year, indicating strong market demand [12][14] Group 5: Xinjie Electric Analysis - Xinjie Electric is a leading provider of industrial automation solutions, with a market share ranking second in China's small PLC market [16][17] - The company reported a revenue of 877 million RMB in the first half of 2025, a year-on-year increase of 10.01% [16] - Xinjie Electric is focusing on large client strategies and expanding its overseas presence, with a nearly 50% increase in overseas orders year-on-year [17]

Investment Rating - The report assigns an "Outperform" rating to the company for the first time [5]. Core Views - The company is a leading provider of industrial automation solutions in China, with a strong market position in PLC and drive systems, and is accelerating its layout in the embodied intelligence industry [1][3]. - The company achieved total revenue of 877 million yuan in the first half of 2025, a year-on-year increase of 10.01%, and a net profit of 127 million yuan, up 0.39% year-on-year [1][20]. - The company is focusing on a large customer strategy and deepening its overseas layout, with overseas orders increasing by nearly 50% year-on-year as of June 2025 [2][10]. Summary by Sections Company Overview - The company has been established since 2008 and has accumulated significant technology in the domestic PLC and drive system sectors, ranking second in the market share of small PLCs among domestic brands in 2024 [1][8]. - Its core products include PLCs, HMIs, servo systems, variable frequency drives, and robots, covering various layers of industrial automation [1][9]. Financial Performance - In the first half of 2025, the company's gross margin was 38.28% and net margin was 14.49%, showing a slight decline compared to the previous year due to increased sales expenses from new business expansions [1][23]. - The company’s revenue is projected to grow from 1.51 billion yuan in 2023 to 2.94 billion yuan in 2027, with a compound annual growth rate (CAGR) of 21.33% [20][43]. Investment and Growth Strategy - The company plans to invest 800 million yuan to build a robot intelligent drive control system project, aiming to accelerate the development of its second growth curve in the embodied intelligence industry [3][36]. - The company is actively developing core components for humanoid robots and has already achieved small-scale sales of related products [3][35]. Market Position and Client Base - The company has established strategic partnerships with leading clients across various sectors, including BYD and CATL in the new energy sector, and has expanded its global presence in regions such as Russia, the Middle East, and Southeast Asia [2][10]. - The company’s main products, including PLCs and drive systems, account for the highest revenue share, with 38.05% and 47.33% respectively in 2024 [10][41]. Future Outlook - The company expects to benefit significantly from the growth of the embodied intelligence industry, with projected net profits of 278 million yuan, 353 million yuan, and 433 million yuan for 2025, 2026, and 2027 respectively [3][43]. - The report estimates a reasonable valuation range for the company's stock between 75.43 and 82.98 yuan, based on projected earnings [5][45].

Investment Rating - The investment rating for Baidu Group is "Outperform the Market" (maintained) [1] Core Insights - Baidu's value is being reassessed in the context of the AI wave, with self-developed chips (Kunlun), AI infrastructure services, and AI application scenarios contributing to its growth. The Kunlun chip's technological strength is becoming evident, AI cloud revenue is rapidly increasing, and the autonomous driving business is expanding internationally due to cost advantages. The monetization potential of applications like Baidu Wenku and Baidu Cloud is significant, and the AI advertising monetization model is gradually being implemented [2]. Summary by Sections Company Overview - Baidu's core advertising business accounts for approximately 50% of revenue, while AI-related businesses (AI cloud, autonomous driving) contribute about 30%. iQIYI accounts for around 20% of revenue. The traditional advertising business is under pressure, while AI businesses are on the rise [2][9]. Kunlun Chip Progress - The demand for domestic AI chips is expected to surge, with projected revenues of approximately 5 billion RMB in 2025 and 10 billion RMB in 2026 for Kunlun chips, of which Baidu holds a 59% stake. The Kunlun P800 chip has a FP16 computing power of 345 TFLOPS, surpassing the A800, and supports large-scale deployments [2][23]. Baidu AI Cloud Progress - In Q2 2025, AI cloud revenue reached 6.5 billion RMB, a year-on-year increase of 27%. The total revenue for 2025 is expected to reach 27.4 billion RMB, with a growth rate of 26%. By 2026, revenue could reach 35 billion RMB, with continued profit improvement [2][30]. Autonomous Driving - Apollo Go - The total order volume for Apollo Go is expected to exceed 10 million in 2025. In Q2 2025, Apollo Go provided over 2.2 million rides, a 148% year-on-year increase, with a cumulative service of over 14 million rides. The business model is profitable in cities like Wuhan, although short-term profit contributions are limited [2]. AI Advertising and Digital Agent Business - By July, AI-generated content accounted for 64% of mobile search results, covering 90% of Baidu App's monthly active users. In Q2 2025, AI-generated advertising revenue increased by 50% quarter-on-quarter, contributing 13% to core online marketing revenue [2]. Financial Forecast - Revenue projections for Baidu have been adjusted upwards for 2025-2027, with expected revenues of 133.6 billion RMB, 143.7 billion RMB, and 154.1 billion RMB respectively. Adjusted net profit estimates for the same period are 21 billion RMB, 24.4 billion RMB, and 28 billion RMB [2].

Investment Rating - The report maintains an "Outperform" rating for the sustainable aviation fuel (SAF) industry, indicating expected performance above the market average [2][7]. Core Insights - The demand for SAF is primarily driven by policy initiatives, particularly in the EU, which mandates a 2% blending ratio by 2025, with a long-term goal of 70% by 2050. The IATA projects that SAF demand could reach 358 million tons by 2050, indicating significant growth potential [3][11]. - There is a notable supply-demand gap in the European SAF market, with consumption expected to reach 1.9 million tons this year against a production capacity of only 1 million tons. This gap is likely to be filled by producers in the Asia-Pacific region, including China, which has the potential to significantly increase its SAF production capacity [3][13]. - The high cost of SAF compared to traditional jet fuel has led to a general reluctance among airlines to adopt it. However, the EU's stringent blending requirements are pushing airlines to increase their SAF procurement, resulting in a rapid price increase for SAF [4][18]. - The report recommends investing in "Zhuoyue New Energy" and related SAF companies, highlighting Zhuoyue as a leading domestic biodiesel producer with significant SAF production capacity planned [4][26]. Summary by Sections Market Dynamics - As of September 17, the price of high-end SAF in China reached $2,480 per ton, a 55% increase from $1,800 per ton at the beginning of the year. This price surge is attributed to the scarcity of SAF raw materials and the unchanged mandatory blending targets [3][21]. - The European market is experiencing a significant increase in SAF consumption, with a projected 216% year-on-year growth, while the production capacity remains limited [13][18]. Policy Framework - The EU has established comprehensive SAF application targets and carbon reduction goals, with regulations mandating a 2% SAF blend starting in 2025 and a long-term goal of 70% by 2050 [5][10]. - Other countries, including the UK, the US, Japan, and South Korea, are also implementing policies to promote SAF usage, with specific blending targets set for the coming years [6][8][9]. Production Capacity - China is expected to play a crucial role in filling the SAF production gap in Europe, with domestic companies planning to establish over 1 million tons of SAF production capacity by the end of 2024 [3][13]. - The report outlines various projects across China, detailing planned and existing SAF production capacities, indicating a robust growth trajectory for the industry [14][17].

证券研究报告 | 2025年09月18日 2025 年 8 月财政数据快评 又到政策蓄力时 经济研究·宏观快评 | 证券分析师： | 田地 | 0755-81982035 | tiandi2@guosen.com.cn | 执证编码：S0980524090003 | | --- | --- | --- | --- | --- | | 联系人： | 王奕群 | | wangyiqun1@guosen.com.cn | | 事项： 1-8 月，全国一般公共预算收入 148198 亿元，同比增长 0.3%。其中，全国税收收入 121085 亿元，同比微 增 0.02%；非税收入 27113 亿元，同比增长 1.5%。 1-8 月，全国一般公共预算支出 179324 亿元，同比增长 3.1%。分中央和地方看，中央一般公共预算本级 支出 26570 亿元，同比增长 8%；地方一般公共预算支出 152754 亿元，同比增长 2.3%。 评论： 一般公共预算收支两端同时边际转弱。收入增速边际回落。8 月一般公共预算收入当月同比 2%，前值 2.6%。 税收收入当月同比 3.4%，前值 5%；非税收入当月同比-3.8%，前值 ...