Investment Rating - The report maintains an "Accumulate" rating for the basic chemical industry [5] Core Viewpoints - The 2025 Nobel Prize in Chemistry was awarded to three scientists for their pioneering contributions in the field of Metal-Organic Frameworks (MOFs), which opens new avenues for material science and addresses global energy, environmental, and health issues [1] - MOFs exhibit excellent physical and chemical properties, including high porosity, large specific surface area, and high thermal and chemical stability, making them suitable for various applications [2] - The report highlights the broad application fields of MOFs, including gas storage and separation, catalysis, energy storage and conversion, and biomedical applications, indicating a promising future for industrialization [3] Summary by Sections 1. Industry Performance - The basic chemical sector showed a mixed performance, with the CITIC basic chemical sector index rising by 0.8%, ranking 13th among all sectors [9] - Key sub-sectors such as phosphate fertilizer and titanium dioxide saw significant gains, while lithium battery chemicals experienced declines [11] 2. Key Product Price Tracking - Notable price increases were observed in aluminum fluoride and coated separators, with increases of 5.86% and 5.56% respectively [16] - Conversely, products like naphtha and urea saw declines, with naphtha prices dropping by 3.60% [18] 3. Sub-industry Dynamics - The polyester filament market faced price fluctuations due to geopolitical risks and weak demand, leading to inventory accumulation [19] - The polyurethane sector experienced a stable to declining market for MDI, with limited impact from external events [19] - The fertilizer market showed weakness, influenced by adverse weather conditions and declining raw material prices [19]

Report Industry Investment Rating - No industry investment rating is provided in the report. Core Viewpoints - The issuance volume of credit bonds decreased seasonally, and the credit spreads of various industries showed mixed trends [1][25]. Summary According to Relevant Catalogs 1. Primary Market 1.1 Issuance Statistics - From September 28 to October 11, 2025, a total of 119 credit bonds were issued, with a total issuance scale of 141.362 billion yuan, a month - on - month decrease of 75.82% [1][11]. - Among them, 39 industrial bonds were issued, with a scale of 64.185 billion yuan, a month - on - month decrease of 75.75%, accounting for 45.40% of the total credit bond issuance scale; 70 urban investment bonds were issued, with a scale of 43.777 billion yuan, a month - on - month decrease of 72.63%, accounting for 30.97%; 10 financial bonds were issued, with a scale of 33.4 billion yuan, a month - on - month decrease of 79.11%, accounting for 23.63% [1][11]. - The average issuance term of credit bonds was 2.44 years, with industrial bonds at 1.63 years, urban investment bonds at 2.80 years, and financial bonds at 3.16 years [2][14]. - The average issuance coupon rate of credit bonds was 2.34%, with industrial bonds at 2.12%, urban investment bonds at 2.45%, and financial bonds at 2.35% [2][18]. 1.2 Cancellation of Issuance Statistics - Three credit bonds were cancelled for issuance during the period [23]. 2. Secondary Market 2.1 Credit Spread Tracking - By industry, among Shenwan primary industries, the largest upward adjustment of AAA - rated industry credit spreads was in the building decoration industry, up 2.6BP, and the largest downward adjustment was in the media industry, down 4BP; for AA + - rated industry credit spreads, the largest upward adjustment was in the chemical industry, up 6.6BP, and the largest downward adjustment was in the non - ferrous metals industry, down 8.6BP; for AA - rated industry credit spreads, the largest upward adjustment was in the non - bank finance industry, up 6.7BP, and the largest downward adjustment was in the machinery and equipment industry, down 5BP [3][25]. - Coal credit spreads generally increased, while steel credit spreads generally decreased. The AAA and AA + - rated coal credit spreads increased by 0.3BP and 0.5BP respectively, and the AAA and AA + - rated steel credit spreads decreased by 1.1BP and 2.7BP respectively [25]. - The credit spreads of urban investment bonds of each rating showed mixed trends, while non - urban investment credit spreads generally increased. The credit spreads of the three - level urban investment bonds decreased by 0.2BP, increased by 0.6BP, and increased by 1.7BP respectively; the credit spreads of the three - level non - urban investment bonds increased by 0.1BP, increased by 2.3BP, and increased by 1.3BP respectively [25]. - The credit spreads of state - owned enterprises showed mixed trends, while those of private enterprises generally increased. The credit spreads of the three - level central state - owned enterprises decreased by 0.7BP, decreased by 0.3BP, and decreased by 1.1BP respectively; the credit spreads of the three - level local state - owned enterprises increased by 0.6BP, increased by 1.8BP, and increased by 2BP respectively; the AAA and AA + - rated private enterprise credit spreads increased by 10.9BP and 0.8BP respectively [25][26]. - Regionally, the credit spreads of urban investment bonds showed mixed trends. The regions with the highest AAA - rated credit spreads were Liaoning, Shaanxi, and Jilin, with spreads of 101, 94, and 91BPs respectively; for AA + - rated, they were Qinghai, Shaanxi, and Gansu, with spreads of 144, 131, and 125BPs respectively; for AA - rated, they were Shaanxi, Sichuan, and Guangxi, with spreads of 154, 154, and 153BPs respectively. In terms of month - on - month changes, the largest upward adjustment of AAA - rated credit spreads was in Henan, up 7.2BP, and the largest downward adjustment was in Shaanxi, down 4.7BP; for AA + - rated, the largest upward adjustment was in Hunan, up 8BP, and the largest downward adjustment was in Yunnan, down 4.9BP; for AA - rated, the largest upward adjustment was in Guangxi, up 26.8BP, and the largest downward adjustment was in Yunnan, down 5BP [25][27]. 2.2 Trading Volume Statistics - The total trading volume of credit bonds was 855.283 billion yuan, a month - on - month decrease of 47.12%. The top three in terms of trading volume were commercial bank bonds, corporate bonds, and medium - term notes. Specifically, the trading volume of commercial bank bonds was 254.914 billion yuan, a month - on - month decrease of 47.74%, accounting for 29.80% of the total credit bond trading scale; the trading volume of corporate bonds was 237.461 billion yuan, a month - on - month decrease of 52.14%, accounting for 27.76%; the trading volume of medium - term notes was 17.722 billion yuan, a month - on - month decrease of 45.30%, accounting for 20.72% [4][28]. 2.3 Actively Traded Bonds This Period - The report selects the top 20 urban investment bonds, industrial bonds, and financial bonds in terms of trading volume for investors' reference [30].

Investment Rating - The report maintains an "Accumulate" rating for the basic chemical industry [5] Core Viewpoints - The 2025 Nobel Prize in Chemistry was awarded to three scientists for their pioneering contributions in the field of Metal-Organic Frameworks (MOFs), which opens new avenues for material science and addresses global energy, environmental, and health issues [1] - MOFs exhibit excellent physical and chemical properties, including high porosity, large specific surface area, and high thermal and chemical stability, making them suitable for various applications [2] - The report highlights the broad application fields of MOFs, including gas storage and separation, catalysis, energy storage and conversion, and biomedical applications, indicating a promising future for their industrialization [3] Summary by Sections 1. Industry Performance - The basic chemical sector showed a mixed performance, with the CITIC basic chemical sector index rising by 0.8%, ranking 13th among all sectors [9] - The top-performing sub-sectors included phosphate and phosphorus chemicals (+5.9%) and potassium fertilizers (+4.9%) [11] 2. Key Product Price Tracking - Notable price increases were observed in aluminum fluoride (+5.86%) and various coated membranes [16] - The report also tracks price declines in products like naphtha (-3.60%) and urea (-3.09%) [18] 3. Sub-industry Dynamics - The report discusses various sub-sectors, including the polyester filament market experiencing price fluctuations and the polyurethane sector facing steady declines [19] - The fertilizer market is noted for its weak performance due to adverse weather conditions affecting agricultural activities [19]

Group 1 - The A-share market is experiencing differentiation, with most major indices declining, while the Shanghai Composite Index saw a slight increase. Mid-cap and small-cap value stocks outperformed, while large-cap growth stocks lagged behind [1][3][16] - The current valuation of the ChiNext 50 and the Wind All A indices is relatively high, with their PE (TTM) percentile exceeding 90% as of October 10, 2025 [1][13][29] Group 2 - Recent policy measures include export controls on key materials, adjustments to the technical requirements for new energy vehicle purchase tax, and the establishment of cloud computing standards [2][20][22] - Economic data from the recent National Day holiday indicates a significant increase in domestic travel, with 888 million trips taken, up 123 million from the previous year, and total spending reaching 809 billion yuan [2][23] Group 3 - The market is expected to enter a phase of wide fluctuations due to high valuations and cautious capital, compounded by uncertainties in Sino-US relations. However, upcoming policy expectations and potential interest rate cuts by the Federal Reserve may provide support [3][28][29] - Mid-term prospects for listed companies' profitability are improving, with signs of stabilization in industrial profits and a potential recovery in Q4 supported by policy measures [3][30] Group 4 - Short-term investment focus should be on high-dividend and consumer sectors, while mid-term attention should shift to TMT (Technology, Media, and Telecommunications) and advanced manufacturing sectors [4][35][36] - Historical trends suggest that during market fluctuations, sectors that previously underperformed may become more attractive, particularly high-dividend and consumer sectors such as banking and utilities [4][36]

1. Report Industry Investment Rating - No relevant information provided 2. Core Viewpoints of the Report - From September 29 to October 10, 2025, the secondary - market prices of China's listed public REITs showed a continuous downward trend. The weighted REITs index closed at 183.91, with a return rate of - 0.47% during the period. Compared with other mainstream large - category assets, REITs ranked relatively low in terms of return rate [1][11]. - The trading volume and turnover rate of public REITs showed differentiation. The consumer infrastructure - type REITs led in terms of the average daily turnover rate during the period. The total net inflow of main funds decreased, indicating a decline in market trading enthusiasm compared with the previous period. The total amount of block trades also decreased compared with the previous period [2][29][30]. - In the primary market, Huaxia Kaide Commercial REIT was listed on September 29, 2025, with an asset type of consumer infrastructure and an issuance scale of 2.87 billion yuan. The status of two REIT projects was updated [4]. 3. Summary According to Relevant Catalogs 3.1 Secondary Market 3.1.1 Price Trends - **Large - category Asset Level**: The secondary - market prices of China's listed public REITs showed a continuous downward trend. The China Securities REITs (closing) and China Securities REITs total return indexes closed at 826.77 and 1058.71 respectively, with return rates of - 0.56% and - 0.54% during the period. The weighted REITs index closed at 183.91, with a return rate of - 0.47%. Compared with other mainstream large - category assets, the return rates from high to low were: gold > convertible bonds > A - shares > pure bonds > REITs > US stocks > crude oil [11]. - **Underlying Asset Level**: In terms of project attributes, the secondary - market prices of property - type and franchise - type REITs both decreased. In terms of underlying asset types, only municipal facilities - type and new infrastructure - type REITs increased. The top three underlying asset types in terms of return rate were municipal facilities, new infrastructure, and ecological environment protection, with weighted indexes of 127.98, 105.1, and 123.97 respectively, and return rates of 0.47%, 0.36%, and - 0.03% respectively [16][17]. - **Single REIT Level**: During the period, public REITs showed mixed performance. 17 REITs rose, 1 remained the same as the previous period, and 57 REITs fell. The top three in terms of increase were Huatai Nanjing Jianye REIT, Hua'an Waigaoqiao REIT, and GF Chengdu Gaotou Industrial Park REIT, with increases of 3%, 1.8%, and 1.01% respectively. The top three in terms of decline were China Merchants Expressway REIT, CICC Vipshop Outlet Mall REIT, and CICC Yinli Consumption REIT, with declines of 3.19%, 2.35%, and 2.02% respectively [23]. 3.1.2 Transaction Scale and Turnover Rate - **Underlying Asset Level**: The transaction scale of public REITs during the period was 1.78 billion yuan. The consumer infrastructure - type REITs led in terms of the average daily turnover rate during the period. The total transaction amount of the 75 listed REITs during the period was 1.78 billion yuan, and the average value of the average daily turnover rate during the period was 0.45%. In terms of transaction amount, the top three REIT asset types were consumer infrastructure, transportation infrastructure, and park infrastructure, with transaction amounts of 775 million, 294 million, and 186 million yuan respectively. In terms of turnover rate, the top three REIT asset types in terms of the average daily turnover rate during the period were consumer infrastructure, new infrastructure, and water conservancy facilities, with rates of 1.20%, 0.70%, and 0.49% respectively [24]. - **Single REIT Level**: The performance of single - REIT transaction scale and turnover rate continued to show differentiation. In terms of trading volume, the top three during the period were Huaxia Kaide Commercial REIT, CICC Vipshop Outlet Mall REIT, and Huaxia Hefei High - tech REIT, with trading volumes of 94 million, 17 million, and 9 million shares respectively. In terms of transaction amount, the top three were Huaxia Kaide Commercial REIT, CICC Vipshop Outlet Mall REIT, and Guojin China Railway Construction REIT, with transaction amounts of 611 million, 73 million, and 53 million yuan respectively. In terms of turnover rate, the top three were Huaxia Kaide Commercial REIT, CICC Vipshop Outlet Mall REIT, and Huaxia Huadian Clean Energy REIT, with turnover rates of 58.93%, 5.67%, and 3.87% respectively [27]. 3.1.3 Main Fund Inflow and Block Trade Situation - **Main Fund Inflow Situation**: The total net inflow of main funds during the period was 9.83 million yuan, indicating a decline in market trading enthusiasm compared with the previous period. From the perspective of different underlying asset REITs, the top three in terms of net inflow of main funds during the period were consumer infrastructure, new infrastructure, and ecological environment protection, with net inflows of 9.76 million, 4.40 million, and 3.13 million yuan respectively. From the perspective of single REITs, the top three REITs in terms of net inflow of main funds during the period were Southern Runze Technology Data Center REIT, Huaxia China Resources Commercial REIT, and Hua'an Zhangjiang Industrial Park REIT, with net inflows of 4.95 million, 3.32 million, and 2.53 million yuan respectively [29]. - **Block Trade Situation**: The total amount of block trades during the period reached 43.10 million yuan, a decrease compared with the previous period. There were block trades on 4 trading days during the period, with a total block - trade transaction amount of 43.10 million yuan. The block - trade transaction amount on Thursday (October 09, 2025) was the highest during the period, reaching 18.48 million yuan. In terms of single REITs, the top three in terms of block - trade transaction amount during the period were ICBC Hebei Expressway REIT, Huaxia Joy City Commercial REIT, and E Fund Guangzhou Development Industrial Park REIT, with transaction amounts of 27.73 million, 6.80 million, and 6.04 million yuan respectively, and corresponding average discount/premium rates of - 0.28%, - 1.78%, and - 1.93% respectively [30]. 3.2 Primary Market 3.2.1 Listed Projects - As of October 10, 2025, the number of China's public REIT products reached 75, with a total issuance scale of 196.619 billion yuan. Among them, the transportation infrastructure - type REITs had the largest issuance scale, reaching 68.771 billion yuan, followed by the park infrastructure - type REITs, with an issuance scale of 31.835 billion yuan [34]. - Huaxia Kaide Commercial REIT was listed on September 29, 2025, with an asset type of consumer infrastructure and an issuance scale of 22.87 billion yuan [4][34]. 3.2.2 Projects to be Listed - According to the project dynamic disclosures of the Shanghai Stock Exchange and the Shenzhen Stock Exchange, there were 17 REITs in the state of being to be listed, including 11 initial - offering REITs and 6 REITs to be expanded. During the period, the project status of Huaxia Hubei Jiaotou Chutian Expressway Closed - end Infrastructure Securities Investment Fund (initial offering) was updated to "feedback provided", and the project status of Huaxia China Resources Commercial Asset Closed - end Infrastructure Securities Investment Fund (expansion) was updated to "declared" [38].

Quantitative Models and Construction - **Model Name**: PB-ROE-50 **Model Construction Idea**: The model combines Price-to-Book ratio (PB) and Return on Equity (ROE) to construct a stock selection strategy[25] **Model Construction Process**: The PB-ROE-50 model selects stocks based on their PB and ROE metrics. Stocks with favorable PB and ROE values are included in the portfolio. The model uses a monthly rebalancing approach to optimize the portfolio[25][26] **Model Evaluation**: The model demonstrates positive excess returns in most stock pools, indicating its effectiveness in capturing value and profitability factors[25][26] - **Model Name**: Institutional Research Tracking Strategy **Model Construction Idea**: This strategy leverages institutional research activities (public and private) to identify stocks with potential excess returns[27] **Model Construction Process**: The strategy tracks stocks that are frequently researched by public and private institutions. Stocks with higher research frequency are included in the portfolio. The portfolio is rebalanced periodically to reflect updated research trends[27][28] **Model Evaluation**: The strategy shows consistent positive excess returns, suggesting that institutional research activities can be a reliable indicator for stock selection[27][28] - **Model Name**: Block Trade Strategy **Model Construction Idea**: The strategy identifies stocks with high block trade activity and low volatility to construct a portfolio[31] **Model Construction Process**: Stocks are selected based on two criteria: high block trade transaction ratios and low 6-day transaction volatility. The portfolio is rebalanced monthly to maintain these characteristics[31][32] **Model Evaluation**: The strategy has mixed results, with negative excess returns in the recent 2-week period, but positive performance over the year[31][32] - **Model Name**: Directed Issuance Strategy **Model Construction Idea**: The strategy focuses on stocks involved in directed issuance events to capture potential investment opportunities[36] **Model Construction Process**: Stocks are selected based on the announcement date of directed issuance events. The strategy considers market capitalization, rebalancing frequency, and position control to construct the portfolio[36][37] **Model Evaluation**: The strategy shows negative excess returns in the recent 2-week period, raising questions about its effectiveness under current market conditions[36][37] Model Backtesting Results - **PB-ROE-50 Model**: - Excess return in CSI 500: -0.82% - Excess return in CSI 800: 1.45% - Excess return in the entire market: 0.75%[25][26] - **Institutional Research Tracking Strategy**: - Public research excess return: 1.03% - Private research excess return: 1.89%[27][28] - **Block Trade Strategy**: - Excess return relative to CSI All Index: -0.57%[31][32] - **Directed Issuance Strategy**: - Excess return relative to CSI All Index: -1.13%[36][37] Quantitative Factors and Construction - **Factor Name**: Liquidity Factor **Factor Construction Idea**: Measures the liquidity of stocks to identify those with higher trading activity[20] **Factor Construction Process**: The liquidity factor is calculated using metrics such as turnover rate and trading volume. Stocks with higher liquidity scores are assigned positive weights[20] **Factor Evaluation**: The factor shows positive returns in the recent 2-week period, indicating its effectiveness in capturing market liquidity trends[20] - **Factor Name**: Leverage Factor **Factor Construction Idea**: Evaluates the financial leverage of companies to identify those with higher risk-adjusted returns[20] **Factor Construction Process**: The leverage factor is derived from financial ratios such as debt-to-equity and interest coverage. Companies with optimal leverage levels are favored[20] **Factor Evaluation**: The factor demonstrates positive returns, suggesting its utility in identifying financially stable companies[20] - **Factor Name**: Profitability Factor **Factor Construction Idea**: Captures the profitability of companies to identify those with strong earnings performance[20] **Factor Construction Process**: The profitability factor is calculated using metrics such as ROE, ROA, and net profit margin. Stocks with higher profitability metrics are given positive weights[20] **Factor Evaluation**: The factor shows positive returns, indicating its effectiveness in identifying profitable companies[20] - **Factor Name**: Valuation Factor **Factor Construction Idea**: Measures the relative valuation of stocks to identify undervalued opportunities[20] **Factor Construction Process**: The valuation factor is derived from metrics such as Price-to-Earnings (P/E) and Price-to-Book (P/B) ratios. Stocks with lower valuation scores are assigned positive weights[20] **Factor Evaluation**: The factor demonstrates positive returns, supporting its use in identifying undervalued stocks[20] - **Factor Name**: Non-linear Market Capitalization Factor **Factor Construction Idea**: Captures the non-linear relationship between market capitalization and stock returns[20] **Factor Construction Process**: The factor is constructed using a non-linear transformation of market capitalization data. Stocks with optimal market capitalization are assigned positive weights[20] **Factor Evaluation**: The factor shows positive returns, indicating its ability to capture market capitalization trends effectively[20] - **Factor Name**: Beta Factor **Factor Construction Idea**: Measures the sensitivity of a stock's returns to market movements[20] **Factor Construction Process**: The beta factor is calculated using historical return data and market indices. Stocks with lower beta values are assigned positive weights[20] **Factor Evaluation**: The factor shows negative returns, suggesting its limited effectiveness in the current market environment[20] - **Factor Name**: Residual Volatility Factor **Factor Construction Idea**: Evaluates the idiosyncratic risk of stocks to identify those with stable performance[20] **Factor Construction Process**: The residual volatility factor is derived from the standard deviation of residuals in a regression model of stock returns against market returns[20] **Factor Evaluation**: The factor shows negative returns, indicating its limited utility in the recent market conditions[20] - **Factor Name**: Growth Factor **Factor Construction Idea**: Captures the growth potential of companies based on their financial performance[20] **Factor Construction Process**: The growth factor is calculated using metrics such as revenue growth and earnings growth. Stocks with higher growth rates are assigned positive weights[20] **Factor Evaluation**: The factor shows negative returns, suggesting its limited effectiveness in the current market environment[20] Factor Backtesting Results - **Liquidity Factor**: Return: 0.36%[20] - **Leverage Factor**: Return: 0.34%[20] - **Profitability Factor**: Return: 0.27%[20] - **Valuation Factor**: Return: 0.18%[20] - **Non-linear Market Capitalization Factor**: Return: 0.18%[20] - **Market Capitalization Factor**: Return: 0.11%[20] - **Beta Factor**: Return: -0.65%[20] - **Residual Volatility Factor**: Return: -0.55%[20] - **Growth Factor**: Return: -0.21%[20]

1. Report Industry Investment Rating No relevant information provided. 2. Core View of the Report - From the beginning of 2025 to October 10, the convertible bond market underperformed the equity market, with the CSI Convertible Bond Index rising by 17.1% and the CSI All-Share Index rising by 23.3%. In the long term, convertible bonds remain relatively high-quality assets due to the persistent pattern of strong demand exceeding supply. However, the current valuation level is relatively high, so attention should be paid to structural opportunities [1][4]. 3. Summary by Relevant Catalogs Market Conditions - From October 9 to 10, 2025 (2 trading days), convertible bonds first rose and then fell. The CSI Convertible Bond Index had a change of 0% (previous trading cycle: +1.6%), and the CSI All-Share Index changed by -0.3% (previous trading cycle: +2.0%). Since the beginning of 2025, the convertible bond market has underperformed the equity market [1]. - By rating, high-rated bonds (AA+ and above), medium-rated bonds (AA), and low-rated bonds (AA- and below) had changes of +0.45%, +0.50%, and -0.31% respectively this week. Low-rated bonds performed poorly [1]. - By convertible bond size, large-scale convertible bonds (bond balance > 5 billion yuan), medium-scale convertible bonds (balance between 500 million and 5 billion yuan), and small-scale convertible bonds (balance < 500 million yuan) had changes of +0.32%, +0.12%, and -0.16% respectively this week. Large-scale convertible bonds performed the best [1]. - By conversion parity, ultra-high parity bonds (conversion value > 130 yuan), high parity bonds (conversion value between 110 and 130 yuan), medium parity bonds (conversion value between 90 and 110 yuan), low parity bonds (conversion value between 70 and 90 yuan), and ultra-low parity bonds (conversion value < 70 yuan) had changes of +0.03%, -0.17%, +0.03%, +0.14%, and +0.15% respectively this week. High parity bonds closed down [2]. Convertible Bond Price, Parity, and Conversion Premium Rate - As of October 10, 2025, there were 420 outstanding convertible bonds (423 at the close on September 30), with a balance of 587.832 billion yuan (589.024 billion yuan at the close on September 30). The average convertible bond price was 132.67 yuan (131.41 yuan at the close on September 30), the average parity was 105.35 yuan (100.18 yuan at the close on September 30), and the average conversion premium rate was 27.6% (27.1% at the close on September 30) [3]. - The conversion premium rate of medium parity convertible bonds (conversion value between 90 and 110 yuan) was 30.1%, higher than the median conversion premium rate of medium parity convertible bonds since 2018 (20.3%) [3]. Convertible Bond Performance and Allocation Directions - This week, convertible bonds first rose and then fell, and the CSI Convertible Bond Index had a change of 0%. Since the beginning of 2025, the convertible bond market has underperformed the equity market. Given the persistent pattern of strong demand exceeding supply in the convertible bond market, convertible bonds remain relatively high-quality assets in the long term. Currently, the overall valuation level is high, so attention should be paid to structural opportunities [4]. Convertible Bond Increase Situation - The top 15 convertible bonds in terms of increase this week include Zhonghuanzhuan 2, Guanzhong Convertible Bond, Haomei Convertible Bond, etc. The increase rates of these convertible bonds range from 6.01% to 20.00% [22].

Investment Rating - The report maintains a "Buy" rating for the company [5]. Core Insights - The company is expected to achieve significant growth in its two-wheeler segment, with projected sales of 2.6 million and 2.39 million units for 2024 and the first half of 2025, respectively, representing year-on-year growth of 77% and 100% [1][27]. - The gross margin is anticipated to continue rising, reaching 21.1% and 23.7% for 2024 and the first half of 2025, placing the company in a leading position within the industry [1][31]. Summary by Sections Two-Wheeler Business - The company focuses on providing a full lifecycle user experience for its core target audience, with a strong emphasis on smart technology as a key differentiator in its product offerings [2][42]. - The company has established a robust brand presence, with a significant portion of its sales coming from younger consumers, who represent 66% of its customer base under 35 years old [2][47]. - The company has successfully optimized its product structure and achieved economies of scale, leading to a continuous increase in gross margins [1][31]. Research and Development System - The company's R&D system is characterized by a high degree of coupling among its organizational structure, mechanisms, and talent, which is crucial for maintaining competitive advantages [3][22]. - The dual-line R&D mechanism allows the company to balance short-term and long-term goals effectively, ensuring continuous innovation [3][22]. Future Outlook - The company plans to expand its store network significantly, with projections of 7,600 and 8,700 stores for 2024 and the first half of 2025, respectively, and aims to reach 9,500 stores by the end of 2025 [3][26]. - Continuous upgrades to its Over-The-Air (OTA) services will enhance user experience throughout the product lifecycle [3][26]. Profit Forecast and Valuation - The company is projected to achieve net profits of 2 billion, 2.7 billion, and 3.5 billion yuan for 2025, 2026, and 2027, respectively, with corresponding P/E ratios of 23, 18, and 14 [4][5].

- The report utilizes a methodology from the industry rotation series to track the configuration signals and business indicators of various industries, including coal, livestock farming, steel, structural materials, and fuel refining industries [9] Quantitative Models and Construction Methods Coal Industry Model - **Model Name**: Coal Industry Profit and Revenue Growth Estimation Model - **Model Construction Idea**: The model estimates monthly revenue and profit growth of the coal industry based on the changes in price and production capacity factors [10] - **Model Construction Process**: - The long-term contract mechanism for thermal coal determines the sales price for the next month based on the price index of the last month - Monthly revenue and profit growth are estimated using the year-on-year changes in price factors and production capacity factors [10] - **Model Evaluation**: The model predicts that the coal industry profit for October 2025 will continue to decline year-on-year due to coal prices being lower than the same period last year [14] Livestock Farming Model - **Model Name**: Livestock Supply and Demand Gap Estimation Model - **Model Construction Idea**: The model uses the relationship between the number of breeding sows and the quarterly pig slaughter rate to estimate the supply-demand gap for pigs six months later [15] - **Model Construction Process**: - The model assumes a stable proportional relationship between quarterly pig slaughter and the number of breeding sows six months prior - Formula: $ \text{Slaughter Coefficient} = \frac{\text{Quarterly Pig Slaughter}}{\text{Breeding Sow Inventory (lagged 6 months)}} $ [15] - Potential production capacity after 6 months is calculated as: $ \text{Potential Production Capacity (6 months later)} = \text{Breeding Sow Inventory (current month)} \times \text{Slaughter Coefficient (6 months prior)} $ [16] - Potential demand after 6 months is calculated as: $ \text{Potential Demand (6 months later)} = \text{Quarterly Pig Slaughter (6 months prior)} $ [16] - **Model Evaluation**: Historical experience shows that the slaughter coefficient method effectively identifies pig price upward cycles [16] Steel Industry Model - **Model Name**: Steel Industry Profit and Unit Profit Estimation Model - **Model Construction Idea**: The model predicts monthly profit growth and calculates unit profit for the steel industry by considering comprehensive steel prices and cost indicators such as iron ore, coke, pulverized coal, and scrap steel [18] - **Model Construction Process**: - Comprehensive steel prices and cost indicators are used to predict monthly profit growth - Unit profit is calculated based on the difference between steel prices and costs [18] - **Model Evaluation**: The model predicts that the steel industry profit for September 2025 will grow year-on-year, but the PMI rolling 12-month average remains flat, maintaining a neutral configuration viewpoint [23] Structural Materials and Building Engineering Model - **Model Name**: Glass and Cement Industry Profit Tracking Model - **Model Construction Idea**: The model tracks profitability changes in the glass and cement manufacturing industries using price and cost indicators, and designs configuration signals based on profitability changes [25] - **Model Construction Process**: - Price and cost indicators are used to track profitability changes - Configuration signals are designed based on profitability changes [25] - **Model Evaluation**: - Glass industry profit turned positive year-on-year in September 2025, leading to an upgrade to a positive configuration signal [30] - Cement industry profit remained flat year-on-year, and no positive signals were observed in new housing starts, maintaining a neutral configuration viewpoint [30] Fuel Refining and Oil Services Model - **Model Name**: Fuel Refining and Oil Services Profit and Configuration Signal Model - **Model Construction Idea**: The model estimates industry profit growth and cracking spreads based on changes in refined fuel prices and crude oil prices, and designs configuration signals based on oil prices, cracking spreads, and new drilling changes [31] - **Model Construction Process**: - Refined fuel price changes and crude oil price changes are used to estimate industry profit growth and cracking spreads - Configuration signals are designed based on oil prices, cracking spreads, and new drilling changes [31] - **Model Evaluation**: - The model predicts that the fuel refining industry profit for September 2025 will grow year-on-year due to lower inventory costs from recent low oil prices [31] - Observations show that oil prices in September 2025 were lower than the same period last year, maintaining a neutral configuration viewpoint for the fuel refining and oil services industries [37][38] Model Backtesting Results Coal Industry Model - **Profit Growth**: Predicted to continue declining year-on-year in October 2025 due to lower coal prices compared to the same period last year [14] Livestock Farming Model - **Breeding Sow Inventory**: 4,038 million heads as of August 2025, slightly decreased month-on-month [17] - **Potential Production Capacity (26Q1)**: 19,361 million heads [17] - **Potential Demand (26Q1)**: 19,476 million heads [17] - **Supply-Demand Balance**: Slightly tight [17] Steel Industry Model - **Profit Growth**: Predicted to grow year-on-year in September 2025 [23] - **PMI Rolling Average**: Remained flat for 12 months, not exceeding the threshold [23] Structural Materials and Building Engineering Model - **Glass Industry Profitability**: Turned positive year-on-year in September 2025 [30] - **Cement Industry Profitability**: Remained flat year-on-year in September 2025 [30] - **Manufacturing PMI Rolling Average**: Remained flat for 12 months [30] - **Housing Sales Area**: Observed a year-on-year decline in August 2025 [30] Fuel Refining and Oil Services Model - **Fuel Refining Industry Profitability**: Predicted to grow year-on-year in September 2025 due to lower inventory costs [31] - **Oil Price**: Observed to be lower than the same period last year in September 2025 [37] - **New Drilling Activity**: No significant year-on-year changes observed in the US [38]

Investment Rating - The report maintains a "Buy" rating for the automotive and automotive parts industry [5]. Core Insights - In Q3 2025, Tesla's global deliveries exceeded expectations, with a year-on-year increase of 7.4% and a quarter-on-quarter increase of 29.4%, reaching 497,000 units. The Model 3 and Model Y standard versions were launched in North America with reduced starting prices [1]. - Xpeng's sales surpassed 40,000 units in September, marking a year-on-year increase of 94.7% and a quarter-on-quarter increase of 10.3% [1]. - NIO's deliveries also showed growth, with a year-on-year increase of 64.1% and a quarter-on-quarter increase of 11.0%, totaling 34,749 units in September [1]. Summary by Sections Tesla and New Forces Sales Tracking - Tesla's global delivery volume reached 497,000 units in Q3 2025, with Model 3 and Y sales contributing significantly [1]. - Xpeng delivered 41,581 units in September, while NIO and Li Auto reported deliveries of 34,749 and 33,951 units, respectively [1]. Order Trends and Delivery Cycles - Tesla's delivery cycles for the domestic Model 3 and Model Y have been extended, indicating high demand as the peak season approaches [2]. - New energy vehicle manufacturers like Li Auto and NIO are also experiencing changes in delivery cycles, with some models seeing extended wait times [2]. Company Recommendations - The report recommends investing in companies such as NIO, Xpeng, SAIC Motor, and Geely Automobile, as well as parts suppliers like Fuyao Glass and Top Group [3]. - It highlights the potential in the robotics and intelligent driving themes, suggesting a focus on companies involved in these sectors [3]. Earnings Forecast and Valuation Table - The earnings per share (EPS) and price-to-earnings (PE) ratios for key companies are provided, with NIO, Xpeng, and SAIC Motor all receiving a "Buy" rating based on their projected performance [4].