Group 1: Report General Information - Report Type: Fixed Income Report [1] - Release Time: October 14, 2025 [1] - Analysts: Liang Weichao, Xie Peng [2] Group 2: Core Views - Core view of the weekly report: Track the liquidity score of individual bonds in different bond sectors based on the bond asset liquidity score of qb. For urban investment bonds, the number of high - grade and high - liquidity bond items with medium - short duration and low rating has increased. For industrial bonds, the number of high - grade and high - liquidity bond items with medium - long duration and medium - high rating has generally remained stable [2]. Group 3: Urban Investment Bonds Quantity and Distribution - By region: The number of high - grade and high - liquidity bond items in Sichuan has increased, while that in Shandong, Tianjin, and Chongqing has generally remained stable, and that in Jiangsu has decreased [2][9]. - By duration: The number of high - grade and high - liquidity bond items within 1 year and 1 - 2 years has increased, while that in 2 - 3 years, 3 - 5 years, and over 5 years has decreased [2][9]. - By implied rating: The number of high - grade and high - liquidity bond items of AA+ and AA - has increased, especially AA -; the number of AAA has generally remained stable, and the number of AA and AA(2) has decreased [2][9]. Yield - By region: The yields of high - grade and high - liquidity bond items in Jiangsu, Shandong, Sichuan, Tianjin, and Chongqing have generally declined, with the decline ranging from 1 - 7bp [11]. - By duration: The yields of high - grade and high - liquidity bond items within 1 year, 1 - 2 years, 2 - 3 years, 3 - 5 years, and over 5 years have generally declined, with the decline ranging from 1 - 9bp [11]. - By implied level: The yields of high - grade and high - liquidity bond items of AAA, AA+, AA, AA(2), and AA - have generally declined, with the decline ranging from 1 - 10bp [11]. Score Changes - Ascending top twenty: The main body levels are mainly AA, concentrated in Jiangsu, Zhejiang, and Shandong, and mainly involve industries such as building decoration and comprehensive [12]. - Descending top twenty: The main body levels are mainly AA, distributed in regions such as Jiangsu and Chongqing, and mainly involve industries such as building decoration, comprehensive, and retail [12]. Group 4: Industrial Bonds Quantity and Distribution - By issuer's industry: The overall situation of transportation, coal, and steel industries has remained stable, while that of real estate and public utilities industries has decreased [3][18]. - By duration: The number of high - grade and high - liquidity bond items within 1 year and over 5 years has decreased slightly; the number of those in 1 - 2 years, 2 - 3 years, and 3 - 5 years has generally remained stable [3][18]. - By implied rating: The number of high - grade and high - liquidity bond items with an implied rating of AA+ has increased, while the number of AAA, AAA -, and AA has decreased, and the number of AAA+ has generally remained stable [3][18]. Yield - By industry: The yields of high - grade and high - liquidity bond items in real estate, public utilities, transportation, coal, and steel industries have generally declined, with the decline ranging from 2 - 9bp [20]. - By duration: The yields of high - grade and high - liquidity bond items within 1 year, 1 - 2 years, 2 - 3 years, 3 - 5 years, and over 5 years have generally declined, with the decline ranging from 2 - 8bp [20]. - By implied level: The yields of high - grade and high - liquidity bond items of AAA+, AAA, AAA -, AA+, and AA have generally declined, with the decline ranging from 3 - 11bp [20]. Score Changes - Ascending top twenty: The industries of the main bodies are mainly building decoration, real estate, and non - ferrous metals, with main body levels of AAA and AA+; the industries of the top twenty bonds are mainly transportation and building decoration [21][22]. - Descending top twenty: The main bodies mainly involve industries such as building decoration, real estate, transportation, and public utilities, with main body levels of AAA and AA+; the industries of the top twenty bonds are mainly transportation and public utilities [22].

Economic Overview - Fengdu County's GDP structure shows that the primary industry accounts for less than 15%, while the secondary industry decreased from 45.57% in 2019 to 38.87% in 2023, and the tertiary industry is approaching 50% and increasing annually[20] - In 2023, Fengdu County's total industrial output value was 136.42 billion yuan, a year-on-year decrease of 13.06%[27] - The county's permanent population was 544,200 at the end of 2023, a decrease of 8,800 from the previous year, with an urbanization rate of 51.89%, up by 0.86 percentage points[29] Fiscal Situation - Fengdu County achieved a general budget revenue of 2.765 billion yuan in 2023, with tax revenue of 1.05 billion yuan, showing a gradual increase in fiscal self-sufficiency[31] - The fiscal self-sufficiency rate reached 39.37% in 2023, placing Fengdu County among the top in Chongqing[32] - Government fund income surged by 95.25% in 2023, totaling 1.535 billion yuan, alleviating previous revenue pressures[37] Debt Pressure - As of the end of 2023, Fengdu County's government debt balance was 16.3 billion yuan, with a limited refinancing space of 163.7 billion yuan[40] - The debt-to-GDP ratio was 40.14%, indicating moderate debt pressure compared to other counties in Chongqing[45] - The county's comprehensive debt ratio was 255%, showing a decrease of 28 percentage points from the previous year, indicating manageable overall debt risk[46] Industry Analysis - Fengdu County has over 20 identified mineral resources, with significant reserves of bauxite, shale gas, and limestone, supporting industrial development[49] - The county has established a clean energy industry centered on wind, hydro, and solar power, with a total installed capacity of 710,000 kilowatts and an annual power generation of approximately 1.8 billion kilowatt-hours[52] - Fengdu's agricultural sector is robust, with a focus on six leading industries, including grain, beef cattle, and citrus, ensuring food security and economic stability[51]

Investment Rating - The industry investment rating is "Outperform the Market" and is maintained [2] Core Views - The agricultural sector has shown resilience, with the agricultural index rising by 1.03% recently, ranking 12th among 31 primary industries [5][13] - The pig price has entered a downward trend, reaching a national average of 10.97 yuan/kg, the lowest in recent years, leading to significant industry losses [6][17] - Policies aimed at controlling production capacity and reducing weights in the pig industry are expected to positively impact prices in the second half of next year [7][19] - The white feather chicken sector faces challenges with high chick prices and low meat prices, resulting in losses for farmers [8][28] - The sugar market continues to decline, with prices dropping to 5835 yuan/ton, while soybean prices show slight fluctuations [32][34] Summary by Sections Market Review - The market has experienced significant fluctuations post-holiday, with the agricultural sector outperforming the broader market [5][13] Livestock Industry Tracking - **Pigs**: Prices have sharply declined, with a significant increase in supply post-holiday and a drop in demand from slaughterhouses [6][17] - **White Feather Chicken**: Chick prices remain high at 3.5 yuan/chick, while meat prices are low at 3.37 yuan/jin, leading to losses for farmers [8][28] Planting Industry Tracking - **Sugar**: Prices have decreased by 10 yuan/ton recently [32] - **Soybeans**: Prices have shown minor fluctuations, with Brazilian soybeans at 3900 yuan/ton and American soybeans at 4385 yuan/ton [32][34] - **Corn**: The average price has dropped to 2247 yuan/ton, a decrease of 81 yuan/ton [32][34]

Industry Investment Rating - The investment rating for the food and beverage industry is "Outperform the Market" and is maintained [1] Core Viewpoints - The performance of the liquor sector improved during the National Day holiday, with stable demand for banquets and better sales in the mass price range. The high-end and mid-low price segments performed relatively well, while the sub-high-end segment faced pressure. Overall, the performance aligns with market expectations, indicating a gradual bottoming out in sales and performance for liquor companies [3][13] - The food and beverage sector index (801120.SL) experienced a slight decline of -0.15% this week, ranking 18th among 30 first-level industries, outperforming the CSI 300 index by 0.36 percentage points. The current dynamic PE for the industry is 21.51 [7][16] Summary by Relevant Sections Weekly Observation - The liquor sales during the National Day holiday showed a general improvement compared to previous months, with stable banquet demand. Top brands like Moutai maintained prices above 1800 RMB, while second and third-tier brands faced price adjustments and inventory pressures. The overall performance is consistent with capital market expectations, indicating a gradual recovery phase for liquor companies [3][13] Industry Performance - The food and beverage sector saw a mixed performance, with 10 sub-sectors, excluding other alcoholic beverages and liquor, showing increases. The highest gain was in soft drinks, which rose by 4.86% [7][16] Key Company Announcements - Wuliangye announced a stock buyback plan, acquiring 6,273,266 shares, representing 0.16% of its total shares, for a total amount of approximately 800 million RMB [22] - Jinsiyuan reported a significant revenue decline of nearly 30% in Q2, aligning with market sales trends [22] Important Industry News - The liquor industry is undergoing a transformation characterized by slower sales, channel restructuring, and increased differentiation, laying the groundwork for long-term development [25]

- The report introduces the **Diffusion Index Model** for industry rotation, which has been tracked for four years. The model is based on momentum strategies to capture industry trends. It showed strong performance in 2021 with excess returns exceeding 25% before experiencing a significant drawdown due to cyclical stock adjustments. In 2022, the strategy delivered stable returns with an annual excess return of 6.12%. However, in 2023 and 2024, the model faced challenges, with annual excess returns of -4.58% and -5.82%, respectively. For October 2025, the model suggests allocating to industries such as non-ferrous metals, banking, communication, steel, electronics, and automobiles[26][30] - The **Diffusion Index Model** is constructed by ranking industries based on their diffusion index values, which reflect upward trends. The top six industries as of October 10, 2025, are non-ferrous metals (0.98), banking (0.951), communication (0.909), steel (0.877), electronics (0.823), and automobiles (0.813). The bottom six industries are food and beverage (0.137), consumer services (0.297), real estate (0.407), coal (0.445), transportation (0.457), and construction (0.489)[27][28][29] - The **Diffusion Index Model** achieved an average weekly return of 2.59%, exceeding the equal-weighted return of the CSI First-Level Industry Index by 0.70%. For October, the model's excess return is -0.37%, while the year-to-date excess return is 4.60%[30] - The report also discusses the **GRU Factor Model** for industry rotation, which utilizes minute-level price and volume data processed through a GRU deep learning network. The model has shown strong adaptability in short cycles but struggles in long cycles and extreme market conditions. Since February 2025, the model has focused on growth industries but has faced difficulties in capturing excess returns due to concentrated market themes[32][38] - The **GRU Factor Model** ranks industries based on GRU factor values. As of October 10, 2025, the top six industries are comprehensive (6.64), building materials (5.21), construction (3.55), textile and apparel (3.31), transportation (2.99), and steel (2.88). The bottom six industries are computing (-41.87), food and beverage (-35.34), electronics (-34.87), non-ferrous metals (-28.25), power equipment and new energy (-26.61), and communication (-22.71)[33][36] - The **GRU Factor Model** achieved an average weekly return of 2.88%, exceeding the equal-weighted return of the CSI First-Level Industry Index by 1.01%. For October, the model's excess return is 1.67%, while the year-to-date excess return is -6.55%[36]

- **Barra style factors**: The report tracks various style factors including Beta, Market Cap, Momentum, Volatility, Non-linear Market Cap, Valuation, Liquidity, Profitability, Growth, and Leverage. Each factor is constructed using specific financial metrics and formulas. For example, the Profitability factor combines analyst forecast earnings price ratio, inverse price-to-cash flow ratio, and inverse price-to-earnings ratio (TTM), among others. The Growth factor incorporates earnings growth rate and revenue growth rate. These factors are used to evaluate stocks based on their historical and financial characteristics [13][14][15]. - **GRU factors**: GRU factors are derived from different training objectives, such as predicting future one-day close-to-close or open-to-open returns. Examples include `close1d`, `open1d`, `barra1d`, and `barra5d`. These factors are constructed using GRU models trained on historical data to forecast short-term stock movements. GRU factors showed strong performance, with most models achieving positive multi-period returns, except for `barra1d`, which experienced some drawdowns [20][28][32]. - **Factor testing methodology**: Factors are tested using a long-short portfolio approach. At the end of each month, stocks are ranked based on the latest factor values, with the top 10% being long positions and the bottom 10% being short positions. The portfolios are equally weighted, and factors are industry-neutralized before testing. This methodology ensures robust evaluation of factor performance across different market conditions [15][16][31]. - **Factor performance results**: - **Style factors**: Valuation, Profitability, and Leverage factors showed strong long performance, while Beta, Liquidity, and Momentum factors performed well on the short side [15][16]. - **Technical factors**: Across various time windows, low momentum and low volatility stocks generally outperformed, while high volatility and high momentum stocks underperformed. For example, the 60-day momentum factor showed a negative return of -3.11% in the last month but a positive return of 2.12% over the last six months [19][26][30]. - **GRU factors**: GRU models like `barra1d` achieved a year-to-date excess return of 5.22%, while `barra5d` and `open1d` also delivered strong multi-period returns. However, `barra1d` experienced a weekly drawdown of -1.65% [20][32][33]. - **Multi-factor portfolio performance**: The multi-factor portfolio outperformed the benchmark (CSI 1000 Index) by 1.35% over the past week. GRU-based models also showed strong excess returns, ranging from 0.68% to 1.60% over the same period. Year-to-date, the `barra1d` model achieved an excess return of 5.22% [32][33][34].

Quantitative Models and Construction Methods - **Model Name**: Diffusion Index Model **Model Construction Idea**: The model is designed to monitor the future critical points of diffusion index changes and provide trading signals based on different methods such as threshold methods and moving average methods [6][37][38] **Model Construction Process**: - The diffusion index is calculated based on the relative price changes of micro-cap stock index constituent stocks over a specific time window. - The horizontal axis represents the percentage change in stock prices from +10% to -10% over the next N days, while the vertical axis represents the length of the retrospective window T days or the future N days. - For example, at a horizontal axis value of 0.95 and a vertical axis value of 15 days, the diffusion index value is 0.04, indicating that if all micro-cap stock index constituent stocks drop by 5% after N=5 days, the diffusion index value will be 0.04. - The model uses three methods to generate trading signals: - **First Threshold Method (Left-side Trading)**: Triggered a buy signal on September 23, 2025, with a closing value of 0.0575 [40] - **Delayed Threshold Method (Right-side Trading)**: Gave a buy signal on September 25, 2025, with a closing value of 0.1825 [44] - **Double Moving Average Method (Adaptive Trading)**: Provided a sell signal on August 4, 2025 [45] **Model Evaluation**: The diffusion index is currently at a medium level, indicating a short-term downward trend but not expected to trigger the 0.1 threshold in the next 10 trading days [37][38] Model Backtesting Results - **Diffusion Index Model**: - Current diffusion index value: 0.50 [37] - First Threshold Method: Buy signal triggered at 0.0575 on September 23, 2025 [40] - Delayed Threshold Method: Buy signal triggered at 0.1825 on September 25, 2025 [44] - Double Moving Average Method: Sell signal triggered on August 4, 2025 [45] --- Quantitative Factors and Construction Methods - **Factor Name**: Leverage Factor **Factor Construction Idea**: Measures the financial leverage of companies to assess their risk and return potential [5][31] **Factor Construction Process**: - The leverage factor is calculated as the ratio of total debt to total equity. - The rank IC for this factor is calculated weekly to evaluate its predictive power for stock returns [31] **Factor Evaluation**: This factor showed a positive rank IC this week, indicating its effectiveness in predicting stock returns [5][31] - **Factor Name**: Free Float Ratio Factor **Factor Construction Idea**: Evaluates the proportion of shares available for trading in the market [5][31] **Factor Construction Process**: - The free float ratio factor is calculated as the ratio of free float shares to total shares outstanding. - Weekly rank IC is used to measure its predictive ability [31] **Factor Evaluation**: This factor demonstrated a positive rank IC this week, suggesting its utility in forecasting stock performance [5][31] - **Factor Name**: Dividend Yield Factor **Factor Construction Idea**: Measures the dividend yield of stocks to identify value opportunities [5][31] **Factor Construction Process**: - The dividend yield factor is calculated as the ratio of annual dividend per share to the current stock price. - Weekly rank IC is computed to assess its predictive power [31] **Factor Evaluation**: This factor showed a positive rank IC this week, indicating its effectiveness in predicting stock returns [5][31] - **Factor Name**: Single-quarter ROE Factor **Factor Construction Idea**: Measures the return on equity for a single quarter to evaluate profitability [5][31] **Factor Construction Process**: - The single-quarter ROE factor is calculated as the ratio of net income to shareholders' equity for a single quarter. - Weekly rank IC is used to measure its predictive ability [31] **Factor Evaluation**: This factor demonstrated a positive rank IC this week, suggesting its utility in forecasting stock performance [5][31] - **Factor Name**: Growth Factor **Factor Construction Idea**: Measures the growth potential of companies based on financial metrics [5][31] **Factor Construction Process**: - The growth factor is calculated using metrics such as revenue growth, earnings growth, and other growth indicators. - Weekly rank IC is computed to assess its predictive power [31] **Factor Evaluation**: This factor showed a positive rank IC this week, indicating its effectiveness in predicting stock returns [5][31] --- Factor Backtesting Results - **Leverage Factor**: Rank IC this week: 0.176, historical average: -0.006 [5][31] - **Free Float Ratio Factor**: Rank IC this week: 0.156, historical average: -0.013 [5][31] - **Dividend Yield Factor**: Rank IC this week: 0.109, historical average: 0.021 [5][31] - **Single-quarter ROE Factor**: Rank IC this week: 0.091, historical average: 0.022 [5][31] - **Growth Factor**: Rank IC this week: 0.091, historical average: -0.003 [5][31]

1. Report Industry Investment Rating No information about the report industry investment rating is provided in the content. 2. Core Viewpoints of the Report - High - frequency economic data focuses on four aspects: production shows a seasonal decline with decreased开工 rates of coke ovens, blast furnaces, PTA, and tires, and an increased PX开工 rate; prices generally weaken with falling prices of crude oil, copper, zinc, and agricultural products, while coking coal and rebar prices rise slightly; the transaction area of commercial housing in 30 cities declines, and the land supply area in 100 cities continues to grow; holiday consumption shows steady growth, with high enthusiasm for residents' travel consumption, increased domestic tourist arrivals, total spending, and cross - regional personnel movement year - on - year, but a large year - on - year decline in movie box office. In the short term, attention should be paid to the incremental policies of the October Plenary Session and the 14th Five - Year Plan, Sino - US trade policies, and the recovery of the real estate market [2][3][36]. 3. Summary by Relevant Catalogs Production: Affected by holidays, the overall enthusiasm declines - **Steel**: The coke oven capacity utilization rate decreased by 0.05 pct, the blast furnace开工 rate decreased by 0.18 pct, and the rebar output decreased by 3.06 tons. As of the week of October 10, the coke oven capacity utilization rate of domestic independent coking plants (230 samples) was 74.95%, the blast furnace开工 rate of steel mills (247 samples) was 84.27%, and the national rebar output was 203.4 tons [9]. - **Petroleum asphalt**: The pre - holiday开工 rate increased by 5.7 pct, reaching the highest level since October 2023. In the week of September 24, the domestic petroleum asphalt plant开工 rate was 40.1% [9]. - **Chemical industry**: The PX开工 rate increased by 0.81 pct compared with the pre - holiday week, while the PTA开工 rate decreased by 2.6 pct. On October 10, the domestic chemical PX开工 rate was 88.23%, and the PTA开工 rate was 74.89% [9]. - **Automobile tires**: The开工 rate of all - steel tires decreased by 21.76 pct in the pre - holiday week, and the开工 rate of semi - steel tires decreased by 27.07 pct. In the week of October 9, the开工 rate of all - steel tires was 43.96%, and the开工 rate of semi - steel tires was 46.51% [10]. Demand: The enthusiasm for National Day tourism remains high, and the movie box office shows a large decline - **Real estate**: The transaction area of commercial housing declines, the inventory - to - sales ratio decreases, the land supply area continues to grow, and the transaction premium rate of residential land increases. In the week of October 5, the commercial housing transaction area in 30 large and medium - sized cities was 124.82 square meters, the inventory - to - sales ratio in 10 large cities was 94.66, the land supply area in 100 large and medium - sized cities was 2648.78 square meters, and the transaction premium rate of residential land in 100 large and medium - sized cities was 5.53% [13]. - **Movie box office**: The total box office during the National Day holiday was 1.835 billion yuan, a year - on - year decrease of 12.8%. The total number of moviegoers was 50.07 million, a year - on - year decrease of 3.9%. In the week of October 5, the total national movie box office revenue was 145.2 million yuan [13]. - **Tourism consumption**: During the National Day holiday, the number of domestic tourists increased by 16.08% year - on - year, and the total spending increased by 15.44% year - on - year. In 2025, the National Day holiday (8 days) saw 888 million domestic tourist arrivals, and the total domestic tourism spending was 809.006 billion yuan [17]. - **Automobile**: In the week of September 27, the average daily retail sales of passenger car manufacturers increased by 32,000 vehicles, and the average daily wholesale sales increased by 56,000 vehicles [18][21]. - **Shipping index**: The SCFI rebounded by 4.12%, the CCFI decreased by 6.68%, and the BDI increased by 1.84%. In the week of October 10, the Shanghai Export Container Freight Index (SCFI) was 1160.42 points, the China Export Container Freight Index (CCFI) was 1014.78 points, and the Baltic Dry Index (BDI) was 1936 points [20][22]. Prices: Crude oil, copper, and agricultural products prices decline, while coking coal and rebar prices rise - **Energy**: The Brent crude oil price fell by 2.79% to $62.73 per barrel. On October 10, the settlement price of Brent crude oil futures (continuous contract) was $62.73 per barrel [24]. - **Coking coal**: The futures price increased by 1.66% to 1165 yuan per ton compared with the pre - holiday level. On October 10, the settlement price of coking coal futures (active contract) was 1165 yuan per ton [24]. - **Metals**: The futures prices of LME copper, aluminum, and zinc changed by - 3.05%, + 1.63%, and - 1.52% respectively compared with the previous week, and the domestic rebar futures price increased by 0.55% compared with the pre - holiday level. On October 10, the closing price of LME copper futures (active contract) was $10374 per ton, the closing price of LME aluminum futures (active contract) was $2746 per ton, the closing price of LME zinc futures (active contract) was $2984.5 per ton, and the settlement price of domestic rebar futures (active contract) was 3107 yuan per ton [24][25]. - **Agricultural products**: The overall price of agricultural products declined compared with the pre - holiday level. The Agricultural Product Wholesale Price 200 Index decreased by 0.29%. The prices of pork, eggs, vegetables, and fruits changed by - 2.80%, - 6.09%, - 1.20%, and + 2.31% respectively compared with the previous week. On October 11, the Agricultural Product Wholesale Price 200 Index was 118.42 [27]. Logistics: Self - driving is the main mode of travel during the holiday, and the number of flights to Hong Kong, Macao, Taiwan, and international destinations shows a large increase - **Holiday travel**: The average daily cross - regional personnel flow during the National Day holiday increased by 6.3% year - on - year. Self - driving was the main mode of travel, and the number of flights to international and Hong Kong, Macao, and Taiwan destinations increased significantly. During the National Day holiday, the total cross - regional personnel flow was 2.433 billion person - times, with an average daily flow of 304 million person - times [29]. - **Subway passenger volume**: The subway passenger volumes in Beijing and Shanghai both decreased. On October 10, the seven - day moving average of Beijing's subway passenger volume was 7.5194 million person - times, and that of Shanghai was 8.0686 million person - times [31]. - **Number of flights**: After the holiday, the number of domestic and international flights decreased. On October 11, the seven - day moving average of domestic (excluding Hong Kong, Macao, and Taiwan) flights was 14039.29 flights, the seven - day moving average of domestic (Hong Kong, Macao, and Taiwan) flights was 383.14 flights, and the seven - day moving average of international flights was 1949 flights [34]. - **Urban traffic**: The peak congestion index in first - tier cities decreased. On October 11, the seven - day moving average of the peak congestion index in first - tier cities was 1.51 [34]. Summary: Holiday consumption shows steady growth The high - frequency economic data focuses on production decline, price weakening, changes in the real estate market, and steady growth in holiday consumption. Short - term attention should be paid to relevant policies and the real estate recovery [36].

Industry Investment Rating - The industry investment rating is "Outperform" [1] Core Insights - The report emphasizes the transition from traditional subscription models to performance-based payment structures in AI applications, particularly through the example of Sierra, which focuses on enhancing customer experience and operational efficiency [5][7] - The breakthrough of general large models in AI is highlighted, showcasing their ability to significantly improve performance in specialized sectors such as healthcare and finance through tailored adjustments [6] - The report suggests that AI will not replace enterprise software but will enhance its value by improving customer experience solutions [7] Summary by Sections Industry Overview - The closing index level is 5752.42, with a 52-week high of 5841.52 and a low of 3911.64 [1] AI Application Insights - Sierra's business model focuses on capturing market share and reducing costs by automating customer service processes, which can lead to significant improvements in customer retention and lifetime value [5] - The report discusses the importance of dynamic model selection in AI systems, which allows for better performance in specific tasks [6] Investment Recommendations - The report recommends several stocks for investment, including: - US stocks: PLTR, TEM, SHOP, SPOT, SNOW [8] - Hong Kong stocks: Alibaba, Tencent Holdings, China Software International, and others [9] - A-share stocks: Dingjie Zhizhi, Shiyuan Co., and various others [9]

Investment Rating - The industry investment rating is "Outperform the Market" and is maintained [1] Core Views - The report highlights that the recent escalation in China-US trade tensions may shift market risk preferences, leading to increased attention on defensive sectors within the building materials industry that have strong domestic demand and high dividends. Segments such as cement, glass, and consumer building materials, which have lagged in performance this year, are expected to benefit if market sentiment shifts towards "high cutting low" [3][4] - Cement demand is gradually recovering but remains limited, with production in August 2025 at 148 million tons, down 6.2% year-on-year. The implementation of policies to limit overproduction is expected to enhance capacity utilization in the medium term [3][8] - The glass industry is experiencing a downward trend in demand due to real estate impacts, but recent policy catalysts have led to price increases and inventory replenishment in the midstream sector. The report anticipates that environmental regulations will not lead to a drastic reduction in capacity but will increase costs and accelerate maintenance [4][13] - The fiberglass sector is benefiting from demand driven by the AI industry, with expectations for significant growth in low-dielectric products. The report is optimistic about the continued upward trend in both volume and price [4] - The consumer building materials sector has reached a profitability bottom, with no further downward price pressure expected. The report notes a strong demand for price increases and profitability improvements, particularly among leading companies [4] Summary by Sections Cement - The cement market is entering its peak season, with overall demand showing slow recovery. The construction sector is affected by weather and demand release timing, leading to a weak recovery in housing construction [8] - The report emphasizes the importance of monitoring companies like Conch Cement and Huaxin Cement [3] Glass - The glass industry is facing a continuous decline in demand influenced by real estate, but recent policy changes have led to price increases and midstream inventory replenishment [4][13] - Companies to watch include Qibin Group [4] Fiberglass - The fiberglass sector is experiencing a boom driven by AI-related demand, with expectations for explosive growth in low-dielectric products [4] Consumer Building Materials - The sector's profitability has bottomed out, with strong calls for price increases and profitability improvements. Companies like Dongfang Yuhong and Sankeshu are highlighted for potential recovery [4]