Investment Rating - The industry investment rating is "Outperform the Market" and is maintained [2] Core Views - The agricultural sector has shown defensive characteristics amid recent market adjustments, with the agricultural index down 3.45%, ranking 9th among 31 primary industries [5][12] - The pig market is experiencing weak prices, with a stabilization around 11.50 CNY/kg, driven by seasonal demand expectations, but overall supply exceeds demand, limiting price increases [5][16] - October saw a significant reduction in pig production capacity, with the number of breeding sows falling below 40 million for the first time in 17 months, indicating a potential for price increases in the second half of 2026 [6][18] - The white feather chicken market is facing stable chick prices but declining meat prices, with a notable decrease in the number of breeding chicks updated, indicating supply chain pressures [30][31] Summary by Sections Market Review - The agricultural sector demonstrated resilience with a smaller adjustment compared to the broader market, as the Shanghai Composite Index and CSI 300 fell by 3.90% and 3.77% respectively [12] - Among agricultural sub-sectors, only aquaculture and seeds saw price increases, while others declined [15] Livestock Industry Tracking Pigs - Prices are fluctuating around 11.50 CNY/kg, with expectations of a demand increase due to seasonal factors, but slaughter rates remain low [16] - Losses in pig farming are increasing, with self-bred pigs losing an average of 136 CNY per head, and purchased piglets losing 235 CNY per head [17] - The Ministry of Agriculture reported a 1.1% decrease in breeding stock in October, with a target of reducing 1 million sows being halfway achieved [18][19] White Feather Chicken - Chick prices remain stable at 3.7 CNY per chick, with average profits around 0.8 CNY per chick, while meat prices have slightly decreased to 3.52 CNY per jin [30] - The number of breeding chicks updated has decreased significantly, with a 19.01% drop compared to the previous year, indicating potential supply issues [30][31] Planting Industry Tracking - Sugar prices have decreased to 5512 CNY/ton, down 148 CNY from the previous week [35] - Soybean prices have also fallen, with Brazilian soybeans at 3816 CNY/ton, down 7.4% [35] - Corn prices have shown an upward trend, reaching 2227 CNY/ton, an increase of 16 CNY from the previous week [37]

行业基本情况 | 收盘点位 | | 7155.23 | | --- | --- | --- | | 52 | 周最高 | 7829.42 | | 52 | 周最低 | 4280.14 | 行业相对指数表现 研究所 分析师:李帅华 SAC 登记编号:S1340522060001 Email:lishuaihua@cnpsec.com 分析师:魏欣 SAC 登记编号:S1340524070001 Email:weixin@cnpsec.com 分析师:杨丰源 SAC 登记编号:S1340525070002 Email:yangfengyuan@cnpsec.com 近期研究报告 证券研究报告：有色金属|行业周报 发布时间：2025-11-25 行业投资评级 强于大市 |维持 《储能市场景气，碳酸锂需求维持高 增》 - 2025.11.17 有色金属行业报告 （2025.11.17-2025.11.21） 宏观扰动加剧，建议逢低做多贵金属 l 投资要点 贵金属：坚定持有，等待下一轮主升浪。贵金属本周继续震荡， 波动有所下降但仍在下降区间。之前我们提示，沪金沪银波动率过高， 高波下或迎来调整，耐心等待买入时机，建议在 ...

- The report tracks the performance of various style factors, including market capitalization, non-linear market capitalization, profitability, momentum, volatility, and beta factors[2] - The construction process involves creating long-short portfolios at the end of each month, going long on the top 10% of stocks with the highest factor values and shorting the bottom 10% with the lowest factor values, with equal weighting[16] - The recent performance shows strong long positions in market capitalization, non-linear market capitalization, and profitability factors, while momentum, volatility, and beta factors had strong short positions[16] Factor Performance Tracking - The fundamental factors showed mixed long-short returns, with static financial factors performing positively, while growth and surprise growth factors performed negatively[3][4][5] - Technical factors had negative long-short returns, with momentum factors showing more significant negative returns, favoring low momentum and low volatility stocks[3][4][5] - GRU factors had weak long-short performance, with the barra1d model showing some pullback, while other models had insignificant returns[3][4][5] CSI 300 Component Stocks Factor Performance - Fundamental factors showed mixed long-short returns, with growth and surprise growth factors performing negatively, while static financial factors performed positively[4] - Technical factors had negative long-short returns, with momentum factors showing more significant negative returns, favoring low momentum and low volatility stocks[4] - GRU factors had mixed long-short performance, with the barra1d model showing significant pullback, while the barra5d and close1d models performed strongly[4] CSI 500 Component Stocks Factor Performance - Fundamental factors showed mixed long-short returns, with static financial factors performing positively, while growth and surprise growth factors performed negatively[5] - Technical factors had negative long-short returns, with short-term factors showing more significant performance, favoring low volatility and low momentum stocks[5] - GRU factors had good long-short performance, with the open1d and barra1d models showing slight pullback, while the close1d and barra5d models performed strongly[5] CSI 1000 Component Stocks Factor Performance - Fundamental factors showed similar long-short returns, with static financial factors performing positively, while growth and surprise growth factors performed negatively[6] - Technical factors had negative long-short returns, favoring low volatility and low momentum stocks[6] - GRU factors had strong long-short performance, with the barra1d model showing some pullback, while the close1d and open1d models performed strongly[6] Long-Only Portfolio Performance - The GRU long-only portfolio showed weak performance, with various models underperforming the CSI 1000 index by 0.54% to 1.12%[7] - The barra5d model performed strongly year-to-date, outperforming the CSI 1000 index by 8.55%[7] - The multi-factor portfolio showed weak performance, underperforming the CSI 1000 index by 0.47%[7] Factor Performance Metrics - Momentum factor: -1.93% (one week), -8.36% (one month), -24.78% (six months), 19.89% (year-to-date), 17.64% (three-year annualized), 17.58% (five-year annualized)[17] - Volatility factor: 1.82% (one week), -2.33% (one month), 16.17% (six months), 6.56% (year-to-date), 7.58% (three-year annualized), -11.09% (five-year annualized)[17] - Beta factor: -1.54% (one week), 5.68% (one month), 0.60% (six months), 19.29% (year-to-date), 7.50% (three-year annualized), 8.99% (five-year annualized)[17] - Liquidity factor: 0.91% (one week), 42.89% (one month), 9.98% (six months), 12.24% (year-to-date), -20.32% (three-year annualized), -24.87% (five-year annualized)[17] - Valuation factor: 0.82% (one week), 0.46% (one month), 0.14% (six months), 3.77% (year-to-date), 14.92% (three-year annualized), 5.46% (five-year annualized)[17] - Growth factor: 0.71% (one week), 2.28% (one month), 2.34% (six months), 3.16% (year-to-date), 49.33% (three-year annualized), -4.78% (five-year annualized)[17] - Leverage factor: 0.35% (one week), 2.37% (one month), 3.68% (six months), 15.17% (year-to-date), 6.40% (three-year annualized), 1.98% (five-year annualized)[17] - Profitability factor: 0.49% (one week), -0.64% (one month), 7.01% (six months), 14.10% (year-to-date), 3.12% (three-year annualized), 0.51% (five-year annualized)[17] - Non-linear market capitalization factor: 4.22% (one week), 0.44% (one month), 3.16% (six months), -32.83% (year-to-date), -38.38% (three-year annualized), -30.29% (five-year annualized)[17] - Market capitalization factor: 5.39% (one week), 0.59% (one month), 2.18% (six months), -37.92% (year-to-date), -40.48% (three-year annualized), -34.25% (five-year annualized)[17]

Quantitative Models and Construction Methods 1. Model Name: Diffusion Index Model - **Model Construction Idea**: The model is based on the principle of price momentum, aiming to capture upward trends in industries and sectors[22][23] - **Model Construction Process**: The diffusion index is calculated for each industry based on its price momentum. The model ranks industries by their diffusion index values and selects the top-performing industries for portfolio allocation. The model has been tracking out-of-sample performance since 2021, with adjustments made monthly or weekly based on updated diffusion index rankings[22][23] - **Model Evaluation**: The model has shown strong performance in capturing industry trends during momentum-driven markets but struggles during market reversals[22][36] 2. Model Name: GRU Factor Model - **Model Construction Idea**: This model leverages minute-level price and volume data processed through a GRU (Gated Recurrent Unit) deep learning network to generate industry factors for rotation strategies[37] - **Model Construction Process**: The GRU model uses historical price and volume data as input to train a deep learning network. The network identifies patterns and generates factors that are used to rank industries. The top-ranked industries are selected for portfolio allocation. The model is updated weekly to reflect changes in the rankings[30][31][37] - **Model Evaluation**: The GRU model performs well in short-term trading environments but has shown limited effectiveness in long-term scenarios. It is also sensitive to extreme market conditions[37] --- Backtesting Results of Models 1. Diffusion Index Model - **Weekly Average Return**: -5.50% - **Excess Return over Equal-Weighted CSI First-Level Industry Index**: -0.42% - **November-to-Date Excess Return**: -1.13% - **Year-to-Date Excess Return**: 1.22%[26][22][23] 2. GRU Factor Model - **Weekly Average Return**: -4.71% - **Excess Return over Equal-Weighted CSI First-Level Industry Index**: 0.35% - **November-to-Date Excess Return**: 2.92% - **Year-to-Date Excess Return**: -2.74%[35][30][31] --- Quantitative Factors and Construction Methods 1. Factor Name: Diffusion Index - **Factor Construction Idea**: The diffusion index measures the momentum of industries by analyzing price trends and ranks industries based on their momentum[22][23] - **Factor Construction Process**: The diffusion index is calculated for each industry using price momentum data. Industries are ranked based on their diffusion index values, and the top-ranked industries are selected for portfolio allocation. The index is updated weekly or monthly to reflect changes in industry momentum[22][23] - **Factor Evaluation**: The factor effectively captures upward trends in industries but may underperform during market reversals[22][36] 2. Factor Name: GRU Industry Factor - **Factor Construction Idea**: The GRU industry factor is derived from minute-level price and volume data processed through a GRU deep learning network to identify patterns and rank industries[37] - **Factor Construction Process**: The GRU model processes historical price and volume data through a deep learning network. The network generates factors that are used to rank industries. The top-ranked industries are selected for portfolio allocation, with updates made weekly[30][31][37] - **Factor Evaluation**: The factor is effective in short-term trading environments but less so in long-term scenarios. It is also sensitive to extreme market conditions[37] --- Backtesting Results of Factors 1. Diffusion Index Factor - **Weekly Average Return**: -5.50% - **Excess Return over Equal-Weighted CSI First-Level Industry Index**: -0.42% - **November-to-Date Excess Return**: -1.13% - **Year-to-Date Excess Return**: 1.22%[26][22][23] 2. GRU Industry Factor - **Weekly Average Return**: -4.71% - **Excess Return over Equal-Weighted CSI First-Level Industry Index**: 0.35% - **November-to-Date Excess Return**: 2.92% - **Year-to-Date Excess Return**: -2.74%[35][30][31]

Quantitative Models and Construction Diffusion Index Model - **Model Name**: Diffusion Index Model [5][17] - **Construction Idea**: The model monitors the market's diffusion index to identify critical turning points for trading signals [5][17] - **Construction Process**: - The diffusion index is calculated based on the relative price movements of constituent stocks within the micro-cap index over a specific time window [37] - The model uses three methods: - **First Threshold Method (Left-Side Trading)**: Triggered when the diffusion index reaches a predefined risk threshold. For example, on November 14, 2025, the index value of 0.925 triggered a sell signal [41] - **Delayed Threshold Method (Right-Side Trading)**: Provides a sell signal when the index value drops below a delayed threshold, such as 0.8975 on November 17, 2025 [46] - **Dual Moving Average Method (Adaptive Trading)**: Generates buy signals based on the crossover of two moving averages, such as the buy signal on October 13, 2025 [47] - **Evaluation**: The model effectively identifies market turning points and provides actionable trading signals [5][17] Small-Cap Low-Volatility 50 Strategy - **Model Name**: Small-Cap Low-Volatility 50 Strategy [7][16][33] - **Construction Idea**: Selects 50 stocks with small market capitalization and low volatility from the micro-cap index [7][33] - **Construction Process**: - Stocks are screened based on market capitalization and volatility metrics [7][33] - Portfolio is rebalanced bi-weekly [7][33] - Transaction costs are set at 0.3% for both buying and selling [7] - **Evaluation**: The strategy demonstrates strong performance in specific market conditions but underperforms during broader market downturns [7][33] --- Model Backtesting Results Diffusion Index Model - **First Threshold Method**: Triggered sell signal at 0.925 on November 14, 2025 [41] - **Delayed Threshold Method**: Triggered sell signal at 0.8975 on November 17, 2025 [46] - **Dual Moving Average Method**: Generated buy signal on October 13, 2025 [47] Small-Cap Low-Volatility 50 Strategy - **2024 Performance**: Annual return of 7.07%, excess return of -2.93% [7][33] - **2025 YTD Performance**: Annual return of 63.78%, weekly excess return of -2.23% [7][33] --- Quantitative Factors and Construction Weekly Factor Performance - **Top 5 Factors**: - **Leverage Factor**: Weekly rank IC of 0.182, historical average of -0.005 [4] - **Free Float Ratio Factor**: Weekly rank IC of 0.138, historical average of -0.012 [4] - **Turnover Factor**: Weekly rank IC of 0.116, historical average of -0.081 [4] - **Liquidity Factor**: Weekly rank IC of 0.075, historical average of -0.041 [4] - **Dividend Yield Factor**: Weekly rank IC of 0.064, historical average of 0.022 [4] - **Bottom 5 Factors**: - **Unadjusted Stock Price Factor**: Weekly rank IC of -0.311, historical average of -0.017 [4] - **Beta Factor**: Weekly rank IC of -0.3, historical average of 0.003 [4] - **Non-Liquidity Factor**: Weekly rank IC of -0.161, historical average of 0.039 [4] - **Inverse PE_TTM Factor**: Weekly rank IC of -0.138, historical average of 0.016 [4] - **Single-Quarter ROE Factor**: Weekly rank IC of -0.089, historical average of 0.021 [4] Additional Weekly Factor Performance - **Top 5 Factors**: - **Logarithmic Market Cap Factor**: Weekly rank IC of 0.225, historical average of -0.034 [16] - **Nonlinear Market Cap Factor**: Weekly rank IC of 0.225, historical average of -0.034 [16] - **Beta Factor**: Weekly rank IC of 0.083, historical average of 0.003 [16] - **Unadjusted Stock Price Factor**: Weekly rank IC of 0.065, historical average of -0.017 [16] - **Past Year Volatility Factor**: Weekly rank IC of 0.06, historical average of -0.033 [16] - **Bottom 5 Factors**: - **Past 10-Day Return Factor**: Weekly rank IC of -0.226, historical average of -0.061 [16] - **Momentum Factor**: Weekly rank IC of -0.196, historical average of -0.006 [16] - **Leverage Factor**: Weekly rank IC of -0.114, historical average of -0.005 [16] - **Single-Quarter Net Profit Growth Factor**: Weekly rank IC of -0.11, historical average of 0.019 [16] - **Standardized Expected Earnings Factor**: Weekly rank IC of -0.104, historical average of 0.013 [16] --- Factor Backtesting Results Weekly Factor Performance - **Leverage Factor**: Weekly rank IC of 0.182 [4] - **Free Float Ratio Factor**: Weekly rank IC of 0.138 [4] - **Turnover Factor**: Weekly rank IC of 0.116 [4] - **Liquidity Factor**: Weekly rank IC of 0.075 [4] - **Dividend Yield Factor**: Weekly rank IC of 0.064 [4] - **Unadjusted Stock Price Factor**: Weekly rank IC of -0.311 [4] - **Beta Factor**: Weekly rank IC of -0.3 [4] - **Non-Liquidity Factor**: Weekly rank IC of -0.161 [4] - **Inverse PE_TTM Factor**: Weekly rank IC of -0.138 [4] - **Single-Quarter ROE Factor**: Weekly rank IC of -0.089 [4] Additional Weekly Factor Performance - **Logarithmic Market Cap Factor**: Weekly rank IC of 0.225 [16] - **Nonlinear Market Cap Factor**: Weekly rank IC of 0.225 [16] - **Beta Factor**: Weekly rank IC of 0.083 [16] - **Unadjusted Stock Price Factor**: Weekly rank IC of 0.065 [16] - **Past Year Volatility Factor**: Weekly rank IC of 0.06 [16] - **Past 10-Day Return Factor**: Weekly rank IC of -0.226 [16] - **Momentum Factor**: Weekly rank IC of -0.196 [16] - **Leverage Factor**: Weekly rank IC of -0.114 [16] - **Single-Quarter Net Profit Growth Factor**: Weekly rank IC of -0.11 [16] - **Standardized Expected Earnings Factor**: Weekly rank IC of -0.104 [16]

证券研究报告：房地产|行业周报 发布时间：2025-11-24 | 行业基本情况 | | --- | | 收盘点位 | | 2271.52 | | --- | --- | --- | | 52 | 周最高 | 2506.48 | | 52 | 周最低 | 1870.99 | 行业相对指数表现 -19% -15% -11% -7% -3% 1% 5% 9% 13% 17% 21% 2024-11 2025-02 2025-04 2025-06 2025-09 2025-11 房地产 沪深300 资料来源：聚源，中邮证券研究所 研究所 分析师:高丁卉 SAC 登记编号:S1340524080001 Email:gaodinghui@cnpsec.com 近期研究报告 行业投资评级 强于大市|维持 《地产数据加速下滑 政策放松预期升 温》 - 2025.11.17 房地产行业报告 （2025.11.17-2025.11.23） 量价承压 信心与预期仍待修复 ⚫ 投资要点 当前房地产行业仍处于调整周期，销售端整体同比延续下滑态 势，政策层面持续加码城市更新与存量盘活，成为推动行业转型的关 键抓手。我们认为目前市场信心 ...

Investment Rating - The report maintains a "Buy" rating for the company [1] Core Insights - The company has demonstrated a strong performance in the first three quarters of 2025, achieving a revenue of 5.071 billion yuan, representing a year-on-year growth of 9.29%, and a net profit attributable to shareholders of 698 million yuan, up 17.51% year-on-year [4][5] - The increase in revenue is attributed to the rising penetration of edge AI applications and the continuous expansion of new product sales [5] - The company has launched several new products compatible with its edge AI model, enhancing its market presence and driving growth in both consumer and business sectors [6][7] Financial Performance - The company reported a historical high in revenue for the first three quarters, with a quarterly revenue of 1.741 billion yuan in Q3, reflecting a year-on-year increase of 7.20% [5] - The net profit for Q3 was 201 million yuan, showing a year-on-year decrease of 13.14% and a quarter-on-quarter decrease of 34.76% due to market conditions affecting storage chip prices and delivery delays [5] - The company expects revenue for 2025, 2026, and 2027 to be 7.064 billion yuan, 8.971 billion yuan, and 10.658 billion yuan respectively, with net profits projected at 1.013 billion yuan, 1.406 billion yuan, and 1.808 billion yuan [8][12] Product and Market Development - The company has seen significant growth in product shipments, with over 14 million units of self-developed edge AI chips shipped, a year-on-year increase of over 150% [7] - Collaborations with major brands like Samsung and Walmart have expanded the company's product offerings and market reach [6][7] - The integration of the chip design team is expected to enhance the company's multi-dimensional communication technology stack, broadening application scenarios [7]

横盘之后，是涨是跌？ ——流动性周报 20251123 证券研究报告：固定收益报告 发布时间：2025-11-24 研究所 分析师:梁伟超 SAC 登记编号:S1340523070001 Email:liangweichao@cnpsec.com 近期研究报告 《从资管信托新规，看银行理财变局— — 机 构 行 为 专 题 二 20251120 》 - 2025.11.21 固收周报 ⚫ 上涨面依然大于下跌面 观点回顾：四季度债市或在震荡中前行。对短端而言，高配置价 值和交易价值是实实在在的，存单年末还将存在一定的供给压力，但 出现负反馈的概率不高，在资金稳定宽松背景下，同业存单利率处于 高配置价值区间，年末有超预期下行的可能。对于长端而言，前期期 限利差的扩张，给予了长端一定的修复空间。依然坚持四季度债市利 多因素多发，但赎回压力持续存在，需要以区间震荡思路做交易的判 断。随着宽松预期的升温，不妨对后期后续的债市行情更乐观点。 债市进入盘整状态，甚至不对风险偏好做出反应。近几周，债市 波动收窄的程度依然超出预期，即收益率波动幅度明显收窄，仅仅因 房地产政策预期等因素而出现小幅波动，甚至在全球风险偏好明显收 ...

Investment Rating - The report assigns a "Buy" rating for the company, marking its first coverage [1]. Core Insights - The company has shown steady revenue growth, with a 15.87% year-on-year increase in revenue for the first three quarters of 2025, reaching 1.784 billion yuan. However, net profit decreased by 12.06% to 172 million yuan due to rising expenses and one-time factors [4][5]. - The company is a leader in the automotive passive safety sector, with a product line that includes safety fabrics and airbags. It is also expanding into the semiconductor and computing power manufacturing sectors, aiming to create a second growth engine [5]. - The acquisition of a 57.84% stake in Fuchuang Youyue is planned, which would enhance the company's control over its operations in the AI and computing power manufacturing industry [6]. Financial Performance - For the first three quarters of 2025, the company reported a revenue of 1.784 billion yuan, with a quarterly revenue of 676 million yuan in Q3, reflecting an 18.34% year-on-year increase. However, net profit for Q3 fell by 43.72% to 36 million yuan [4]. - The company expects revenues of 2.7 billion yuan, 3.2 billion yuan, and 3.8 billion yuan for 2025, 2026, and 2027 respectively, with net profits projected at 387 million yuan, 573 million yuan, and 775 million yuan for the same years [6][9]. - The company’s financial ratios indicate a projected increase in earnings per share (EPS) from 0.84 yuan in 2024 to 2.35 yuan in 2027, alongside a decrease in price-to-earnings (P/E) ratio from 54.62 to 19.56 over the same period [9][12].

Investment Rating - The investment rating for the company is "Buy" and is maintained [1] Core Insights - The company plans to issue up to 19,013,200 convertible bonds to raise a total of no more than 1.901 billion yuan, which will be used for global R&D center construction, edge AI and supporting chips, automotive chips, and motion control chip development and industrialization projects [4][5] - The company has shown initial success in industrial interconnection and automotive electronics, with a revenue of 2.176 billion yuan for the first three quarters, a year-over-year decrease of 8.02%, but a net profit of 276 million yuan, reflecting a year-over-year increase of 54.98% [6] - The company is expected to achieve revenues of 3.05 billion yuan, 3.47 billion yuan, and 4.03 billion yuan for the years 2025, 2026, and 2027 respectively, with net profits of 400 million yuan, 530 million yuan, and 710 million yuan for the same years [7] Company Overview - The latest closing price of the company's stock is 71.82 yuan, with a total market capitalization of 16.7 billion yuan and a circulating market value of 9.7 billion yuan [3] - The company has a debt-to-asset ratio of 22.9% and a price-to-earnings ratio of 65.29 [3]