[Table_Title] 东鹏饮料(605499.SH/09980.HK) 出海起航，扬帆远行 [Table_Summary] 核心观点： 盈利预测： | [Table_ 单位 Finance] :人民币百万元 | | 2023A | 2024A | 2025E | 2026E | 2027E | | --- | --- | --- | --- | --- | --- | --- | | 营业收入 | | 11,263 | 15,839 | 20,953 | 26,233 | 31,397 | | 增长率（ ） | % | 32.4% | 40.6% | 32.3% | 25.2% | 19.7% | | EBITDA | | 2,691 | 4,186 | 5,792 | 7,373 | 8,798 | | 归母净利润 | | 2,040 | 3,327 | 4,528 | 5,668 | 6,853 | | 增长率（ ） | % | 41.6% | 63.1% | 36.1% | 25.2% | 20.9% | | EPS（元/股） | | 5.10 | 6.40 | 8.71 | 10.10 | 12 ...

Investment Rating - The industry investment rating is "Buy" [2] Core Insights - The report provides a comprehensive analysis of 163 regional banks in Zhejiang Province, highlighting their asset and liability structures, profitability, and asset quality [6][20] - The asset growth rate for major city commercial banks in Zhejiang is 9.4%, which is lower than the 14.2% growth rate of listed city commercial banks, while major rural commercial banks show an asset growth rate of 8.0%, exceeding the 6.7% growth rate of listed rural commercial banks [6][25] - The report indicates that the loan-to-asset ratio for city commercial banks is projected to reach 55.5% in 2024, an increase of 95 basis points year-on-year, while rural commercial banks will see a decrease to 59.3%, down 19 basis points [31] - Profitability metrics show that the average Return on Assets (ROA) for city commercial banks in Zhejiang is 0.78%, slightly above the average of listed city commercial banks, while rural commercial banks have an average ROA of 0.82%, which is below the average of listed rural commercial banks [6][31] - The asset quality of regional banks in Zhejiang is reported to be better than that of listed banks, with non-performing loan ratios lower by 16 basis points for city commercial banks and 9 basis points for rural commercial banks compared to their listed counterparts [6][31] Summary by Sections Section 1: Economic Structure of Zhejiang Province - Zhejiang Province is focused on high-quality development and aims to become a model for common prosperity [13] - The province's GDP is heavily concentrated in cities like Hangzhou, Ningbo, and Wenzhou, with Hangzhou accounting for 24.3% of the total GDP in 2025 [15] Section 2: Overview of 163 Regional Banks - The report categorizes the banks into city commercial banks, rural banks, and others, with a total of 163 banks in the region [20] - The distribution of registered capital among these banks is relatively balanced, with 63 banks having over 500 million yuan in registered capital [22] Section 3: Asset and Liability Structure - The asset growth of major city and rural commercial banks has been declining since 2019, with city banks showing a growth rate of 9.4% in the first half of 2025 [25] - The liability structure indicates that customer deposits account for 77.5% of liabilities for city commercial banks, which is higher than the 66.2% for listed city banks [44] Section 4: Profitability and Asset Quality - The average ROE for city commercial banks in Zhejiang is 11.98%, slightly lower than the average of listed city banks [6][31] - The report highlights that the non-performing loan ratio for city commercial banks is lower than that of listed banks, indicating better asset quality [6][31]

[Table_Page] 公司深度研究|汽车服务 [Table_Title] 中国汽研（601965.SH） 卡位稀缺，受益于中国汽车标准做大做强 [Table_Summary] 核心观点： 盈利预测： | [Table_ 单位 Finance] :人民币百万元 | 2023A | 2024A | 2025E | 2026E | 2027E | | --- | --- | --- | --- | --- | --- | | 营业收入 | 4,007 | 4,697 | 4,967 | 5,866 | 7,171 | | 增长率（ % ） | 21.8% | 17.2% | 5.8% | 18.1% | 22.3% | | EBITDA | 1,306 | 1,678 | 1,956 | 2,278 | 2,673 | | 归母净利润 | 825 | 908 | 1,060 | 1,216 | 1,466 | | 增长率（ % ） | 19.7% | 10.0% | 16.8% | 14.7% | 20.6% | | EPS（元/股） | 0.82 | 0.90 | 1.06 | 1.21 | 1.46 | | ...

[Table_Page] 公司深度研究|电子 [公司评级 Table_Invest] 买入 当前价格 49.01 元 合理价值 62.66 元 报告日期 2026-02-03 基本数据 [Table_BaseInfo] | 总股本/流通股本（百万股） | 360.00/182.25 | | --- | --- | | 总市值/流通市值（百万元） | 17643.60/8932.07 | | 一年内最高/最低（元） | 53.00/24.98 | | 30 日日均成交量/成交额（百万） | 10.20/462.26 | | 近 3 个月/6 个月涨跌幅（%） | 36.82/65.80 | [Table_PicQuote] 相对市场表现 证券研究报告 [Table_Title] 兴福电子（688545.SH） 本土湿电子化学品龙头，充分受益存储需求提升 [Table_Summary] 核心观点： | [Table_ 单位 Finance] :人民币百万元 | 2023A | 2024A | 2025E | 2026E | 2027E | | --- | --- | --- | --- | --- | --- | ...

Core Insights - The coal industry is expected to see an improvement in supply-demand dynamics starting in 2026 after a generally loose supply in 2025 [1] Group 1: Coal Sector Review - The coal sector outperformed the market in January, with a cumulative increase of 8.3% year-to-date, surpassing the CSI 300 index by 6.7 percentage points [16] - The coal sector's price-to-earnings (PE) ratio is currently at 15.7 times, ranking 5th among all sectors, indicating a relatively high valuation [20][26] - The coal sector's price-to-book (PB) ratio stands at 1.51 times, also reflecting a historical high level [24] Group 2: Coal Market Overview - In December, electricity consumption remained flat year-on-year, while coal imports increased by approximately 12% [29] - Domestic coal prices in January showed stability, with power coal prices rising slightly by 2.1% or 14 RMB/ton compared to the end of December [29] - International coal prices saw a notable increase, with Newcastle's 6000 kcal thermal coal price rising by 3.8% to 110.1 USD/ton [45] Group 3: Domestic Demand and Supply - In 2025, domestic coal production increased by 1.2% year-on-year, while coal imports decreased by 9.6% [56] - The total coal production in 2025 reached 483.2 million tons, with significant contributions from Shanxi, Inner Mongolia, and Shaanxi [56] - The demand for electricity in 2025 grew by 5.0%, with the industrial sector showing varied growth rates [46] Group 4: Key Companies and Financial Analysis - Key companies in the coal sector include China Shenhua, Yanzhou Coal, and Shaanxi Coal, all rated as "Buy" with robust dividend policies [6][7] - Financial metrics for these companies indicate a favorable outlook, with expected earnings per share (EPS) growth and attractive valuation ratios [7]

Investment Rating - The report maintains a "Buy" rating for the company, with a current price of 7.16 HKD and a target value of 8.66 HKD [4]. Core Insights - The company has signed a land acquisition agreement in Egypt to expand its production capacity, marking a new chapter in its global capacity layout. The transaction amount is 30.4 million USD, funded by the company's own resources. The site is located in the New October Industrial Zone, covering approximately 800,000 square meters, aimed at enhancing the company's apparel and fabric business in Egypt [8]. - The establishment of production capacity in Egypt is expected to help mitigate geopolitical risks and provide more flexible and reliable production solutions for global customers. The advantages include zero tariffs for exports to Europe and the U.S., improved rapid response capabilities, and various tax incentives to lower production costs [8]. - The company forecasts EPS of 0.08, 0.09, and 0.11 USD per share for 2025, 2026, and 2027, respectively. Based on comparable company valuations and considering the company's strong growth momentum and operational resilience, a 12x PE ratio is applied for 2026, leading to a reasonable value of 8.66 HKD per share [8]. Financial Summary - Revenue (million USD): 2,177 in 2023, projected to grow to 3,319 by 2027, with a CAGR of 11.6% from 2025 to 2027 [3]. - EBITDA (million USD): Expected to increase from 291 in 2023 to 424 by 2027 [3]. - Net profit (million USD): Forecasted to rise from 163 in 2023 to 302 by 2027, with a growth rate of 15% in 2027 [3]. - EPS: Expected to grow from 0.06 in 2023 to 0.11 in 2027 [3]. - ROE: Projected to improve from 11.4% in 2023 to 15.4% in 2027 [3].

- The report introduces the "Alpha Factor Database" developed by the Guangfa Financial Engineering team, which is based on MySQL 8.0 and integrates over a decade of research experience. The database includes fundamental factors, Level-1 medium-frequency factors, Level-2 high-frequency factors, machine learning factors, and alternative data factors, supporting strategies such as long-short, index enhancement, ETF rotation, asset allocation, and derivatives[1][9][11] - The "agru_dailyquote" factor, a deep learning factor, is analyzed for its performance across various indices and timeframes. For the entire market with monthly rebalancing, its RankIC averages are 5.30% (1 week), -3.44% (1 month), 11.41% (1 year), and 13.63% (historical). Its historical win rate is 90.85%[4][54][55] - The "DL_1" factor, another deep learning factor, shows RankIC averages of 8.44% (1 week), -4.38% (1 month), 13.69% (1 year), and 13.66% (historical) in the entire market with monthly rebalancing. Its historical win rate is 86.80%[4][54][55] - The "fimage" factor, also a deep learning factor, has RankIC averages of 6.14% (1 week), 2.47% (1 month), 3.80% (1 year), and 5.06% (historical) in the entire market with monthly rebalancing. Its historical win rate is 77.44%[4][54][55] - The "keyperiod_ret_zero" factor, a Level-2 high-frequency factor, demonstrates negative RankIC averages of -8.25% (1 week), -6.39% (1 month), -5.32% (1 year), and -5.39% (historical) in the entire market with monthly rebalancing. Its historical win rate is 85.69%[4][54][55] - The "real_var" factor, a minute-frequency factor, shows negative RankIC averages of -5.14% (1 week), -3.61% (1 month), -7.94% (1 year), and -8.87% (historical) in the entire market with monthly rebalancing. Its historical win rate is 73.73%[4][54][55] - The "bigbuy_bigsell" factor, a Level-2 high-frequency factor, achieves positive RankIC averages of 5.71% (1 week), -3.56% (1 month), 6.80% (1 year), and 9.63% (historical) in the entire market with monthly rebalancing. Its historical win rate is 77.85%[4][54][55] - The "Amihud_illiq" factor, a minute-frequency factor, shows positive RankIC averages of 5.82% (1 week), -7.52% (1 month), 10.48% (1 year), and 10.70% (historical) in the entire market with monthly rebalancing. Its historical win rate is 73.59%[4][54][55]

Quantitative Models and Construction Methods Macro Indicator Trend Model - **Model Name**: Macro Indicator Trend Model - **Construction Idea**: Establish the relationship between macro indicators and asset performance by analyzing the trend of macro indicators and their impact on monthly asset returns[17][18] - **Construction Process**: - Use monthly moving averages of macro indicators to classify them into upward or downward trends - Apply T-test to determine whether the distribution of monthly returns of assets differs significantly under upward and downward trends - Formula: $ t = \frac{\overline{R_1} - \overline{R_2}}{\sqrt{\frac{(n_1-1)S_1^2 + (n_2-1)S_2^2}{n_1+n_2-2}(\frac{1}{n_1} + \frac{1}{n_2})}} \sim t_{n_1+n_2-2} $ - $\overline{R_1}$ and $\overline{R_2}$: Average monthly returns under upward and downward trends - $S_1$ and $S_2$: Standard deviations of monthly returns under upward and downward trends - $n_1$ and $n_2$: Number of months under upward and downward trends[17][18] - **Evaluation**: Effectively identifies macro indicators with significant impacts on asset returns[17][18] Technical Perspective Model - **Model Name**: Technical Perspective Model - **Construction Idea**: Evaluate asset trends, valuation, and fund flows using historical data and specific calculation methods[22][23][25] - **Construction Process**: - **Trend**: Use closing prices or LLT indicators to calculate trend indicators. Assign +1 for upward trends and -1 for downward trends[22] - **Valuation**: Calculate equity risk premium (ERP) as the reciprocal of PE(TTM) minus the 10-year government bond yield. Define historical 5-year percentile as: $ (Current ERP - Historical 5-year ERP Minimum) / (Historical 5-year ERP Maximum - Historical 5-year ERP Minimum) $ Assign scores based on percentile levels: +2 for >90%, +1 for 70%-90%, 0 for 30%-70%, -1 for 10%-30%, -2 for <10%[23][25] - **Fund Flows**: Calculate monthly active net inflows for indices and assess marginal changes. Assign +1 for positive changes and -1 for negative changes[26] - **Evaluation**: Provides a comprehensive view of asset trends, valuation, and fund flows[22][23][25] Fixed Proportion + Macro Indicators + Technical Indicators Combination Model - **Model Name**: Fixed Proportion + Macro Indicators + Technical Indicators Combination Model - **Construction Idea**: Adjust asset weights based on macro and technical indicators while maintaining a fixed proportion baseline[36][40] - **Construction Process**: - Set baseline weights for equity, bonds, commodities, and currency assets - Adjust weights monthly based on macro and technical indicator signals[36][40] - **Evaluation**: Balances fixed proportion allocation with dynamic adjustments for improved performance[36][40] Controlled Volatility + Macro Indicators + Technical Indicators Combination Model - **Model Name**: Controlled Volatility + Macro Indicators + Technical Indicators Combination Model - **Construction Idea**: Limit annualized volatility to 6% while dynamically adjusting weights based on macro and technical indicators[46][50] - **Construction Process**: - Use risk parity as the baseline weight - Adjust weights monthly based on macro and technical indicator signals[46][50] - **Evaluation**: Reduces volatility while maintaining competitive returns[46][50] Equity Style Rotation Models - **Model Name**: Equity Style Rotation Models (Large/Small Cap and Growth/Value) - **Construction Idea**: Adjust weights between equity styles based on macro and technical indicators[57][58] - **Construction Process**: - Set baseline weights for large/small cap and growth/value styles - Adjust weights monthly based on macro and technical indicator signals[57][58] - **Evaluation**: Captures style rotation opportunities for enhanced returns[57][58] --- Model Backtesting Results Macro Indicator Trend Model - **Annualized Return**: Not explicitly provided - **Maximum Drawdown**: Not explicitly provided - **Annualized Volatility**: Not explicitly provided Technical Perspective Model - **Annualized Return**: Not explicitly provided - **Maximum Drawdown**: Not explicitly provided - **Annualized Volatility**: Not explicitly provided Fixed Proportion + Macro Indicators + Technical Indicators Combination Model - **Annualized Return**: 10.20%[40] - **Maximum Drawdown**: 9.27%[40] - **Annualized Volatility**: 6.14%[40] Controlled Volatility + Macro Indicators + Technical Indicators Combination Model - **Annualized Return**: 10.46%[50] - **Maximum Drawdown**: 7.37%[50] - **Annualized Volatility**: 5.54%[50] Large/Small Cap Rotation Model - **Annualized Return**: 14.30%[61] - **Maximum Drawdown**: 49.10%[61] - **Annualized Volatility**: 22.30%[61] Growth/Value Rotation Model - **Annualized Return**: 14.43%[68] - **Maximum Drawdown**: 45.18%[68] - **Annualized Volatility**: 21.57%[68]

[Table_Page] 公告点评|家用电器 证券研究报告 [Table_Title] 科沃斯（603486.SH） 海外扫地机高增，国内经营承压静待拐点 [Table_Summary] 核心观点： 盈利预测： | [Table_Finance] | 2023A | 2024A | 2025E | 2026E | 2027E | | --- | --- | --- | --- | --- | --- | | 营业收入 | 15,502 | 16,542 | 19,221 | 22,091 | 25,140 | | 增长率（ % ） | 1.2% | 6.7% | 16.2% | 14.9% | 13.8% | | EBITDA | 1,056 | 1,698 | 2,262 | 2,615 | 3,087 | | 归母净利润 | 612 | 806 | 1,765 | 2,123 | 2,537 | | 增长率（ % ） | -64.0% | 31.7% | 118.9% | 20.3% | 19.5% | | EPS（元/股） | 1.06 | 1.40 | 3.07 | 3.69 | 4.41 | | 市 ...

[Table_Page] 投资策略|点评报告 2026 年 2 月 3 日 证券研究报告 [Table_Title] 晨会精选 ——观点全追踪（2 月第 2 期） [Table_Summary] 报告摘要： bilulu@gf.com.cn 识别风险，发现价值 请务必阅读末页的免责声明 [分析师： Table_Author]郑恺 SAC 执证号：S0260515090004 SFC CE No. BUU989 021-38003559 zhengkai@gf.com.cn 分析师： 耿正 SAC 执证号：S0260520090002 021-38003660 gengzheng@gf.com.cn 请注意，耿正并非香港证券及期货事务监察委员会的注册 持牌人，不可在香港从事受监管活动。 [联系人： Table_Contacts] 毕露露 ⚫ 电子：AI 记忆为 Agent 的核心底层能力。Agent 时代 Memory 负责 跨轮次、跨任务的状态连续性，沉淀"我是谁"的个性画像，"从哪里 来"的交互历史及"要到哪里去"的目标与反馈闭环。Agent 通常可分 为四类记忆：工作记忆用于当前任务的临时信息存取与推理（ ...