Revenue and Expenditure Overview - In the first ten months of 2025, national general public budget revenue reached 186,490 billion yuan, a year-on-year increase of 0.8%[6] - National general public budget expenditure was 225,825 billion yuan, with a year-on-year growth of 2%[6] Fiscal Spending Decline - In October 2025, the year-on-year growth rate of broad fiscal expenditure plummeted to -19.1%, a decrease of 21.4 percentage points compared to September[1] - The budget completion rate for broad fiscal expenditure in October was 5.6%, lower than 7.2% in 2024 and the five-year average of 6.2%[7] Factors Contributing to Decline - The decline in fiscal expenditure is attributed to three main factors: high base effect from 2024, revenue decline, and a decrease in government debt financing[1] - Broad fiscal revenue in October fell by -0.6%, a drop of 3.8 percentage points from September, with government fund revenue down by -18.4%[4] Government Debt Financing - Government net financing in October 2025 decreased by 5,602 billion yuan year-on-year, contributing to the slowdown in fiscal expenditure growth[3] - The rapid use of fiscal funds in 2025, including special bonds and support for commercial banks, has been largely completed by mid-August[3] Future Outlook - With the implementation of 500 billion yuan in new policy financial tools and another 500 billion yuan in local debt limits, there may be a recovery in fiscal expenditure growth towards the end of the year[4] - The support from "quasi-fiscal" funds is expected to accelerate as these funds are deployed in key sectors like digital economy and artificial intelligence[4]

Revenue and Expenditure Overview - In the first ten months of 2025, the national general public budget revenue was 186,490 billion yuan, a year-on-year increase of 0.8%[6] - National general public budget expenditure reached 225,825 billion yuan, a year-on-year increase of 2%[6] Fiscal Spending Decline - In October 2025, the year-on-year growth rate of broad fiscal expenditure dropped to -19.1%, a decrease of 21.4 percentage points from September[1] - The completion rate of the broad fiscal expenditure budget in October was 5.6%, lower than 7.2% in 2024 and the five-year average of 6.2%[7] Factors Contributing to Decline - The decline in fiscal expenditure was attributed to a high base effect from 2024, a drop in revenue, and a decrease in government debt financing[1] - Broad fiscal revenue in October fell by -0.6%, a decline of 3.8 percentage points compared to September[4] Government Debt Financing - Government net financing in October 2025 decreased by 5,602 billion yuan year-on-year, contributing to the slowdown in fiscal expenditure growth[12] - The rapid use of fiscal funds in 2025, including special bonds and other projects, limited the available financing for October[12] Future Outlook - With the introduction of 5,000 billion yuan in new policy financial tools and local debt limits, there may be a recovery in fiscal expenditure growth towards the end of the year[18] - The support from "quasi-fiscal" funds is expected to accelerate as these funds are deployed in key sectors like digital economy and artificial intelligence[14]

Investment Rating - The investment rating for the airline industry is "Overweight" indicating a positive outlook for the sector [2][5]. Core Insights - The October National Day holiday has driven an increase in travel demand, with passenger transport volume reaching approximately 68.41 million, a year-on-year growth of 6.7% compared to 2024 [2]. - Domestic capacity increased by 2.1% year-on-year, while domestic passenger flow grew by 4.5% [2]. - The average aircraft utilization rate in October was 7.99 hours per day, reflecting a 1.4% increase year-on-year [2]. - Airlines are increasing capacity, with passenger turnover growth outpacing capacity growth. For instance, China Eastern Airlines and China Southern Airlines both saw a 7% increase in ASK (Available Seat Kilometers) compared to 2024 [2][3]. - The international market has shown recovery, with international flights reaching approximately 60,000, recovering to 90% of the levels seen in 2019 [2]. - The report highlights a significant increase in international capacity for airlines like China Eastern Airlines and Spring Airlines, with year-on-year ASK growth of 14% and 153% respectively compared to 2019 [2][3]. - The report suggests that the airline industry is at a turning point, with potential for significant improvement in airline profitability due to rising demand and constrained supply [2]. Summary by Sections Domestic Market - Overall demand growth is outpacing capacity growth, with Spring Airlines showing significant increases in both capacity and volume [2]. - ASK and RPK for major airlines like China Southern and China Eastern have shown positive growth compared to 2024 and 2019 [3]. International Market - Major airlines have exceeded 2019 levels in international operations, with significant year-on-year growth in ASK and RPK for airlines like China Eastern and Spring Airlines [2][3]. Regional Market - Capacity and volume recovery is uneven across regions, with China Southern and China Eastern showing strong recovery compared to 2019 [2][3]. Investment Analysis - The report emphasizes the unprecedented challenges in the aircraft manufacturing chain and the aging fleet, predicting a continued supply constraint over the next 5-10 years [2]. - The report recommends focusing on the airline sector, highlighting strong supply logic and elastic demand, with specific recommendations for airlines such as China Eastern, China Southern, and Spring Airlines [2][5].

Group 1 - The steel industry is expected to see improved profitability due to three main factors: declining raw material prices, supply-side adjustments, and resilient demand from manufacturing [3][5][9] - The West Simandou iron ore project is set to commence production in November 2025, significantly increasing iron ore supply and contributing to a downward trend in iron ore prices [3][71] - Government policies aimed at reducing overcapacity and promoting energy efficiency are expected to accelerate the exit of outdated production capacity, leading to a more optimized supply structure in the steel industry [3][16][10] Group 2 - Demand for steel is projected to stabilize in the construction sector, while manufacturing demand remains resilient, particularly for flat steel and special steel products [3][19][25] - The overall steel demand in China is forecasted to decline slightly, with total demand expected to be 9.05 billion tons in 2025, a decrease of 0.11% from 2024 [19][20] - The construction sector's share of steel demand is decreasing, while the manufacturing sector's share is increasing, indicating a shift in consumption patterns [3][19] Group 3 - The report highlights that the profitability of steel companies is recovering, with a stronger performance expected in flat steel compared to long steel products [3][85][82] - The average profit margin for steel companies is projected to improve as cost pressures ease, with a focus on companies with stable demand and low valuations [3][87][90] - Investment recommendations include focusing on companies like Baosteel, Nanjing Steel, and Hualing Steel, which are expected to benefit from the shift towards manufacturing [3][95][94]

Quantitative Models and Construction Methods 1. Model Name: Mean-Variance Model - **Model Construction Idea**: The model determines the optimal portfolio by balancing expected returns and risks, based on the mean and variance of asset returns[8] - **Model Construction Process**: 1. Define the portfolio return as a random variable 2. Use the expected return ($E[R]$) and variance ($Var[R]$) to measure the portfolio's performance 3. Solve the optimization problem to maximize expected return for a given level of risk or minimize risk for a given level of return - Formula: $ \text{Minimize: } \sigma_p^2 = \sum_{i=1}^n \sum_{j=1}^n w_i w_j \sigma_{ij} $ $ \text{Subject to: } \sum_{i=1}^n w_i = 1 $ Where $w_i$ is the weight of asset $i$, $\sigma_{ij}$ is the covariance between assets $i$ and $j$[8] - **Model Evaluation**: Flexible in adjusting portfolios based on expected returns and risks, but struggles to incorporate new market dynamics and subjective views[8] 2. Model Name: Black-Litterman Model - **Model Construction Idea**: Combines the Bayesian framework with the mean-variance model to incorporate subjective views into the portfolio optimization process[8] - **Model Construction Process**: 1. Start with a prior distribution of expected returns based on market equilibrium 2. Incorporate subjective views as additional constraints 3. Use the Bayesian approach to update the prior distribution with subjective views to form a posterior distribution - Formula: $ \Pi = \tau \Sigma w_{mkt} $ $ E[R] = \left( \tau \Sigma^{-1} + P^T \Omega^{-1} P \right)^{-1} \left( \tau \Sigma^{-1} \Pi + P^T \Omega^{-1} Q \right) $ Where $\Pi$ is the implied equilibrium return, $\tau$ is a scaling factor, $\Sigma$ is the covariance matrix, $w_{mkt}$ is the market portfolio weights, $P$ is the view matrix, $\Omega$ is the uncertainty matrix, and $Q$ is the view vector[8] - **Model Evaluation**: Flexible and allows integration of subjective views, but requires strong assumptions about return distributions and is computationally complex[8] 3. Model Name: Risk Parity Model - **Model Construction Idea**: Focuses on balancing the risk contribution of each asset in the portfolio rather than their weights[7] - **Model Construction Process**: 1. Calculate the risk contribution of each asset: $RC_i = w_i \cdot \sigma_i \cdot \rho_{i,p}$ 2. Adjust weights to equalize the risk contributions across all assets - Formula: $ RC_i = w_i \cdot \sigma_i \cdot \rho_{i,p} $ Where $RC_i$ is the risk contribution of asset $i$, $w_i$ is the weight of asset $i$, $\sigma_i$ is the standard deviation of asset $i$, and $\rho_{i,p}$ is the correlation between asset $i$ and the portfolio[7] - **Model Evaluation**: Enhances risk control and can incorporate multiple risk dimensions, but lacks a mechanism to optimize returns and may struggle with unrecognized risks[7] 4. Model Name: All-Weather Model (Bridgewater) - **Model Construction Idea**: Aims to achieve stable performance across all economic environments by focusing on risk parity under growth and inflation sensitivity[11] - **Model Construction Process**: 1. Classify assets based on their sensitivity to growth and inflation 2. Allocate weights to achieve risk parity across these dimensions - Formula: Not explicitly provided, but the model emphasizes balancing risk rather than returns[11] - **Model Evaluation**: Stable allocation structure with a focus on low-risk assets, but may underperform in specific market conditions due to its heavy reliance on bonds and cash[15] --- Model Backtesting Results 1. Mean-Variance Model - **Maximum Drawdown**: Exceeded 4% in some periods (e.g., 2018-2019), but quickly recovered[57] - **Sharpe Ratio**: Higher than benchmarks in optimistic scenarios, demonstrating strong risk-adjusted returns[57] 2. Black-Litterman Model - **Maximum Drawdown**: Similar to the mean-variance model, with better adaptability to subjective views[57] - **Sharpe Ratio**: Improved compared to the mean-variance model due to the integration of subjective views[57] 3. Risk Parity Model - **Maximum Drawdown**: Generally lower than the mean-variance model, reflecting its focus on risk control[57] - **Sharpe Ratio**: Moderate, as the model does not explicitly optimize returns[57] 4. All-Weather Model - **Maximum Drawdown**: Comparable to fixed-ratio models, with a focus on stability[15] - **Sharpe Ratio**: Similar to benchmarks, reflecting its conservative allocation[15] --- Quantitative Factors and Construction Methods 1. Factor Name: Monthly Frequency Slicing - **Factor Construction Idea**: Use historical slices of monthly data to reflect maximum drawdown and market sentiment[41] - **Factor Construction Process**: 1. Extract rolling 20-day returns for each year 2. Use the bottom 20% quantile to estimate pessimistic scenarios and maximum drawdown - Formula: $ \text{Max Drawdown} = \text{Min} \left( \frac{P_t - P_{peak}}{P_{peak}} \right) $ Where $P_t$ is the price at time $t$, and $P_{peak}$ is the peak price[41] - **Factor Evaluation**: Effective in capturing extreme market conditions, but limited in predicting long-term trends[41] 2. Factor Name: BootStrap State Space - **Factor Construction Idea**: Use BootStrap sampling to create a state space of asset returns under different scenarios[45] - **Factor Construction Process**: 1. Sample historical data with replacement to create new sequences 2. Calculate return distributions for pessimistic, neutral, and optimistic scenarios - Formula: $ F = B - \alpha \cdot C $ Where $F$ is the objective function, $B$ is the expected return under risk constraints, $C$ is the penalty for exceeding risk constraints, and $\alpha$ is the penalty parameter[50] - **Factor Evaluation**: Provides a robust framework for scenario analysis, but computationally intensive[45] --- Factor Backtesting Results 1. Monthly Frequency Slicing - **Maximum Drawdown**: Successfully captured extreme drawdowns in historical data, with 90% coverage for A-shares and Hong Kong stocks[40] - **Sharpe Ratio**: Not explicitly provided, but the factor is more focused on risk control[40] 2. BootStrap State Space - **Maximum Drawdown**: Achieved a 4% maximum drawdown target in most scenarios, with only minor deviations in extreme conditions[57] - **Sharpe Ratio**: Optimized under different scenarios, with higher ratios in optimistic environments[57]

证 券 研 究 报 告 红利回报稳中有进 燃气降本蓄势待发 2026年公用事业行业投资策略 证券分析师：王璐 A0230516080007 朱赫 A0230524070002 2025.11.18 投资分析意见 ◼ 火电：容量电价改善盈利稳定性，火电分红能力有望提升 www.swsresearch.com 证券研究报告 2 ◼ 水电：秋汛偏丰利好今冬明春水电蓄能，看好折旧到期和财务费用改善提升利润空间 • 2025年9月秋汛来水同比大幅增长，上游蓄水量显著提高，保障今冬明春发电量无虞。水电进入资本开支下降阶段，降息周期中可通过债务结构优化等方式降低 利息支出。2026年是三峡水电站机组折旧到期的小高峰，打开盈利空间。推荐长江电力、国投电力、川投能源和华能水电等大水电公司。 • 火电稳定的容量收入来源有效对冲了电量电价波动的风险，使火电企业的盈利结构从 "单一电量依赖" 转向 "电量收入 + 容量收入 + 辅助服务收入" 的多元 化模式。推荐煤电一体化的国电电力、内蒙华电，产业链布局的广州发展。以及大机组占比高的大唐发电、华能国际（A+H）、华电国际。 ◼ 核电：商业模式类比水电，成长性提升估值水平 • 核电燃 ...

Group 1 - The report highlights a strong recovery in the cement and fiberglass sectors, with unique performance from various consumer building materials stocks driven by anti-involution, specialty fabrics, and overseas expansion [3][11]. - In 2026, the outlook for the building materials industry includes accelerated overseas expansion, benefiting companies that have adjusted their channel, product, and sales structures over the past four years [3][11]. - The report identifies key companies to watch, including Huaxin Cement, Keda Manufacturing, and Western Cement, which are positioned well for overseas growth [3][17]. Group 2 - The building materials sector outperformed the CSI 300 index with a cumulative increase of 22.35% from the beginning of 2025 to November 14, 2025, driven by high demand for specialty fiberglass and other catalysts [8][11]. - The report notes that the cement and fiberglass sectors have achieved profit recovery, with the fiberglass sector showing significant revenue growth [11][17]. - The report emphasizes the importance of overseas markets, particularly in Africa, where population growth and urbanization present substantial opportunities for building materials companies [27][35]. Group 3 - The report discusses the transformation of distribution channels in the consumer building materials sector, highlighting companies like Sanhe Tree and Dongpeng Holdings that have successfully adapted to market changes [3][17]. - The consumer building materials segment is expected to benefit from a recovery in domestic demand, with companies like China Liansu and Beixin Building Materials showing potential for growth [3][11]. - The report indicates that the fiberglass sector is experiencing stable profit improvements, with companies like China Jushi and China National Building Material expected to perform well [3][17]. Group 4 - The report outlines the significant growth potential in the fiberglass market, with expectations for continued high demand for specialty fabrics [3][17]. - The report highlights the competitive advantage of Chinese companies in the global market, particularly in cement production, where China accounts for 47% of global output [34][35]. - The report emphasizes the importance of overseas expansion for companies like Huaxin Cement and Keda Manufacturing, which are actively increasing their production capacities in emerging markets [42][54].

Investment Rating - The industry investment rating is "Overweight" indicating a positive outlook for the sector compared to the overall market performance [8]. Core Insights - The report emphasizes that intelligence will be a key theme in the market for 2026, with investment opportunities extending beyond smart driving to areas like Robotaxi. A data closed loop is identified as the core starting point for achieving full-stack self-research, which differs fundamentally from mere data collection [1][3]. - The establishment of a data closed loop is crucial for filtering effective information from massive data, enabling machines to understand data, feedback to correct models, and perform OTA updates for secondary verification. This requires not only data ownership but also the ability to identify data gaps and utilize data to enhance models [1][3]. - The report suggests that the scale of the data closed loop team (e.g., whether it reaches a hundred members) and related investments should be key indicators for assessing a company's commitment and capability for self-research [1][3]. Summary by Sections Data Closed Loop - The report highlights that when algorithm models are truly driven by PB-level data, it will create a competitive barrier that is difficult to replicate. Even if competitors acquire model architectures or poach key personnel, lacking a substantial underlying data accumulation will hinder their ability to replicate similar algorithm capabilities in the short term [2][4]. - Building a solid data closed loop is expected to provide companies with a certainty of competitive advantage for six months to a year. Companies like Xiaopeng, Li Auto, and Huawei are noted to have established a leading advantage in the smart driving sector, with a high degree of technical moat [2][4]. Investment Recommendations - The report recommends focusing on domestic strong alpha manufacturers such as BYD, Geely, and Xiaopeng, as well as companies that represent the trend of intelligence like Huawei's HarmonyOS. Attention is also drawn to companies like JAC Motors and Seres, with specific recommendations for Li Auto, Kobot, Desay SV, and Jingwei Hengrun [2]. - For state-owned enterprise integration, the report suggests monitoring SAIC Motor, Dongfeng Motor Group, and Changan Automobile. Additionally, it highlights component companies with strong performance growth and capabilities for overseas expansion, recommending Fuyao Glass, New Spring, Fuda, Shuanghuan Transmission, and Yinlun [2].

Group 1 - The beauty and personal care sector has shown a recovery in 2025, with the SW Beauty Index rebounding after a decline from 2022 to 2024, achieving a maximum increase of over 15% and becoming a key area in new consumption [3][9][10] - The cosmetics segment is characterized by intense competition among brands, with domestic brands making significant strides in R&D and distribution, while international brands are adopting localized strategies to regain market share [3][20][25] - The medical beauty market is transitioning from a blue ocean to a red ocean, with domestic companies expected to become major competitors by focusing on affordable and specialized products [3][19][24] Group 2 - The e-commerce operation sector is undergoing a transformation, with companies like RuYuchen and Shuiyang Co. leveraging brand incubation and AI to create new growth avenues [3][19] - Key investment recommendations include domestic brands with strong channel and brand matrices such as MaoGePing, ShangMei Co., and PoLaiYa, as well as companies in the medical beauty sector like AiMeiKe and LongZi Co. [3][19][24] - The report emphasizes the importance of brand matrix construction and product innovation in the cosmetics industry, with companies like ShangMei Co. and PoLaiYa leading the way [3][31][40] Group 3 - The skincare and makeup market is expected to enter a phase of consolidation, with strong brands likely to thrive while weaker ones may struggle [23][24] - The market share of domestic brands is increasing, with a notable decline in the market share of international brands, indicating a significant opportunity for domestic players [25][30] - The report highlights the importance of adapting to changing consumer preferences and channel dynamics, with a focus on online platforms and promotional strategies to enhance brand visibility [48][52][53]

Core Insights - The insurance sector is characterized by a "high elasticity" label, with a significant profit increase driven by investment performance, as evidenced by a 68% year-on-year profit growth in Q3 2025, with investment performance contributing 79% of the pre-tax profit increment for the first three quarters [3][11][12] - The "14th Five-Year Plan" emphasizes strong rule of law, strict regulation, risk mitigation, and development promotion, indicating a strategic focus on enhancing the legal framework and regulatory environment for the insurance industry [3][27][28] - The ongoing "anti-involution" policy is expected to boost dividend insurance, while property insurance is undergoing comprehensive governance to improve high-risk insurance types [3][19][27] - The strategic positioning of insurance assets is evolving, with a notable increase in stock and fund investments by listed insurance companies, projected to reach an additional 875.2 to 943.4 billion yuan in A-shares from 2025 to 2027 [3][11][19] - The insurance sector's valuation recovery is anticipated to continue, with recommendations to focus on undervalued, high-elasticity stocks such as China Life, Ping An, and others [3][19][21] Review of Performance - The insurance sector index has risen by 13.5% since the beginning of the year, underperforming the CSI 300 index by 4.1 percentage points [6][10] - In Q3 2025, the total net profit of listed insurance companies reached 426 billion yuan, a year-on-year increase of 33.5%, with significant contributions from investment performance [11][12][19] Policy Outlook - The "15th Five-Year Plan" outlines key directions for the insurance industry, focusing on high-quality development, technological independence, and comprehensive reform [24][28] - The regulatory environment is expected to remain stringent, with a focus on risk mitigation and the promotion of sustainable growth in the insurance sector [27][31] Liability and Asset Management - The "anti-involution" policy is driving a shift towards dividend insurance, while property insurance is seeing a rationalization of competition [3][19][27] - The strategic focus on asset allocation is expected to enhance the investment capabilities of insurance funds, with a projected increase in equity market allocations [3][11][19] Investment Recommendations - The report suggests maintaining a focus on undervalued, high-elasticity stocks within the insurance sector, highlighting companies such as China Life and Ping An as key investment opportunities [3][19][21]