证券研究报告 | 2026年01月11日 2026年01月10日 美股市场速览 弱于大市 资金涌入科技巨头，小盘消费开始发力 价格走势：整体回升，小盘与消费发力 本周，标普 500 涨 1.6%，纳斯达克涨 1.9%。 风格：小盘成长（罗素 2000 成长+4.7%）＞小盘价值（罗素 2000 价值+4.5%） ＞大盘价值（罗素 1000 价值+2.5%）＞大盘成长（罗素 1000 成长+0.9%）。 21 个行业上涨，3 个行业下跌。上涨的主要有：零售业（+8.4%）、耐用消 费品与服装（+5.2%）、材料（+4.9%）、食品与主要用品零售（+4.1%）、 商业和专业服务（+3.7%）；下跌的主要有：技术硬件与设备（-3.2%）、公 用事业（-1.5%）、电信业务（-0.2%）。 资金流向：资金集中涌入科技巨头 本周，标普 500 成分股估算资金流（涨跌额 x 成交量）为+130.2（亿美元， 下同），上周为-30.2，近 4 周为+202.6，近 13 周为+201.8。 20 个行业资金流入，4 个行业资金流出。资金流入的主要有：半导体产品与 设备（+27.6）、技术硬件与设备（+17.2）、零售业（+ ...

Employment Data Overview - December non-farm payrolls increased by 50,000, below the expected 60,000, while the unemployment rate fell to 4.4%[2] - The labor force participation rate declined to 62.4%, which statistically suppresses the unemployment rate, diluting its actual significance[4] Employment Sector Insights - Private sector added 37,000 jobs, with leisure and hospitality, and education and healthcare contributing 88,000 jobs combined, significantly boosting overall non-farm employment[14] - Job losses were evident in the goods-producing sectors, with construction, manufacturing, and mining losing 11,000, 8,000, and 2,000 jobs respectively, indicating weakening demand in the real economy[14] Wage Trends - Average hourly earnings in the service sector rose by 3.7% year-on-year, while goods-producing sectors saw a 4.1% increase, driven more by structural factors than by demand[20] - The increase in average wages reflects a structural effect where low-wage positions are being eliminated, raising the average wage of remaining employees[20] Monetary Policy Outlook - The probability of a rate cut in January is near zero, with the Federal Reserve likely to maintain a cautious stance due to the current employment and inflation dynamics[24] - The Fed's policy decisions will be influenced by upcoming inflation data and potential changes in the Federal Open Market Committee (FOMC) membership, which could reshape market expectations[24]

证券研究报告 | 2026年01月10日 炼油炼化点评 中国石化与中国航油重组，有望加速国内 SAF 应用 行业研究·行业快评 石油石化 投资评级：优于大市（维持） 证券分析师： 杨林 010-88005379 yanglin6@guosen.com.cn 执证编码：S0980520120002 证券分析师： 薛聪 010-88005107 xuecong@guosen.com.cn 执证编码：S0980520120001 证券分析师： 董丙旭 0755-81982570 dongbingxu@guosen.com.cn 执证编码：S0980524090002 事项： 2026 年 1 月 8 日，经国务院批准，中国石化集团公司与中国航油集团公司实施重组。 国信化工观点： 1）中国石化与中国航油重组，有利于保证我国航煤产业链韧性；2）中国石化与中国航油重组后，航空燃 料生产、销售、加注等环节可实现优势互补，增强我国航空燃料产业国际竞争力；3）中国石化与中国航 油重组后，有利于促进可持续航空燃料（SAF）在国内的推广应用。 4）投资建议：推荐在航空煤油生产领域具备优势的【中国石油】及布局生物柴油及可持续航空燃料 ...

Group 1: AIDC Power Equipment Industry - The AIDC power equipment sector is expected to benefit from the surge in data center construction driven by major tech companies, with significant investments planned by firms like Samsung SDS, Tesla, Amazon, and Oracle [1][28] - The demand for power equipment in the AI era is projected to experience explosive growth, with key companies to watch including Jinpan Technology, Xinte Electric, Igor, Hewei Electric, Shenghong Co., and Zhongheng Electric [1][28] Group 2: Lithium Battery Industry - The lithium battery industry is undergoing a transformation with the ongoing efforts to eliminate excess capacity and improve competitive dynamics, which is expected to enhance profitability for companies in the sector [2][68] - Solid-state battery technology is accelerating towards industrialization, with expectations for expanded production lines and increased testing in 2026, laying the groundwork for mass application from 2027 to 2030 [2][69] - Key companies to focus on in the lithium battery sector include CATL, EVE Energy, Zhongchuang航, Zhuhai Guanyu, Enjie, Dingsheng Technology, and Xiamen Tungsten [2][69] Group 3: Wind Power Industry - The domestic wind power sector is anticipated to see a 10%-20% increase in new installations in 2026, supported by saturated orders and stable pricing [3][53] - The profitability of wind turbine manufacturers is expected to recover gradually, with exports contributing positively to performance [3][54] - Key companies in the wind power sector include Goldwind Technology, Tiansheng Wind Energy, Sany Renewable Energy, and Dongfang Cable [3][54] Group 4: Energy Storage Industry - Global energy storage demand is projected to reach 404 GWh in 2026, representing a 38% year-on-year increase, driven by strong domestic market demand and supportive government policies in emerging markets [2][94][96] - The U.S. is expected to see significant growth in large-scale storage installations due to rising energy demands from data centers and ongoing power supply shortages [2][94][96] - Companies to watch in the energy storage space include CATL, EVE Energy, Deye Technology, Hewei Electric, Shenghong Co., and Kelu Electronics [2][94] Group 5: Power Grid Equipment Industry - The power grid equipment sector is expected to experience increased demand due to accelerated approvals and tenders for ultra-high voltage projects, with significant opportunities for companies involved in converter stations and related equipment [3][36] - The implementation of the 2025 version of the State Grid's smart meter standards is anticipated to lead to a price recovery in 2026, with head companies expected to see high growth in overseas revenues and orders [3][36] - Key companies in the power grid equipment sector include Sifang Co., Jinpan Technology, Siyuan Electric, and Huaming Equipment [3][36] Group 6: Photovoltaic Industry - The photovoltaic industry is expected to see improvements in supply-demand dynamics due to policies aimed at reducing excess capacity, with a focus on leading companies in the silicon material segment [83][84] - Innovations such as silver-free materials and perovskite solar cells are anticipated to drive cost reductions and technological advancements in the industry [83][84] - Companies to monitor in the photovoltaic sector include GCL-Poly Energy, Tongwei Co., and Juhua Materials [83][84]

基金投资价值分析 一键布局港股通+红利+低波——标普港股通低波红利指数投资价值 分析 一键布局港股通+红利+低波 经济增速下行，低利率时代，红利策略未来依然有效：随着经济增速的下行， 能够持续提供较高的成长性的公司越来越难找到，因此投资者更倾向于寻找 相对确定性更高的资产，有利于红利策略。红利策略具有"类债券"属性， 在低利率期间红利策略的性价比相对较高。当前利率水平保持较低水平，为 配置红利类策略好时机。 证券研究报告 | 2026年01月10日 政策推动红利资产吸引力提升：随着政策支持为红利资产注入长期估值重塑 动能，上市公司分红力度持续加大。近年来，上市公司分红金额逐年增长， 尤其是 2024 年以来，上市公司分红力度明显加大。后续随着分红制度的持 续完善，红利资产的股息吸引力将进一步提升。 红利资产内部，港股红利低波产品具备更好投资性价比：相比 A 股，港股红 利的股息率仍更具吸引力。对比其他红利类指数的估值和股息率，截至 2025 年 12 月 31 日，标普港股通低波红利指数（SPAHLVCP.SPI）的近 12 个月 股息率为 5.6%，PE（TTM）为 5.7 倍，在估值和股息率上具备比较优势 ...

Group 1 - The report highlights that all four active quantitative strategies outperformed the equity mixed fund index this week, with absolute returns of 4.86% for the Excellent Fund Performance Enhancement Portfolio, 5.13% for the Exceeding Expectations Selected Portfolio, 5.39% for the Brokerage Golden Stock Performance Enhancement Portfolio, and 5.98% for the Growth and Stability Portfolio [1][2][17] - Year-to-date, the Excellent Fund Performance Enhancement Portfolio ranks in the 42.03 percentile among active equity funds, while the Exceeding Expectations Selected Portfolio ranks in the 38.48 percentile, the Brokerage Golden Stock Performance Enhancement Portfolio ranks in the 35.18 percentile, and the Growth and Stability Portfolio ranks in the 28.46 percentile [1][2][17] Group 2 - The Excellent Fund Performance Enhancement Portfolio is constructed by benchmarking against active equity funds rather than broad indices, utilizing quantitative methods to enhance performance based on the holdings of top-performing funds [3][18] - The Exceeding Expectations Selected Portfolio is built by screening stocks based on exceeding expectations events and analyst profit upgrades, focusing on both fundamental and technical criteria to select stocks that show strong support [4][25] - The Brokerage Golden Stock Performance Enhancement Portfolio uses a stock pool from brokerage recommendations, optimizing the combination to minimize deviation from the stock pool while aiming to outperform the equity mixed fund index [5][33] - The Growth and Stability Portfolio employs a two-dimensional evaluation system for growth stocks, prioritizing stocks closer to their earnings report dates and using multi-factor scoring to select high-quality stocks [6][40] Group 3 - The report provides performance statistics for each strategy, indicating that the Excellent Fund Performance Enhancement Portfolio achieved an annualized return of 21.40% from 2012 to 2025, outperforming the equity mixed fund index by 9.85% [55] - The Exceeding Expectations Selected Portfolio recorded an annualized return of 35.09% from 2010 to 2025, exceeding the equity mixed fund index by 23.98% [60] - The Brokerage Golden Stock Performance Enhancement Portfolio achieved an annualized return of 21.71% from 2018 to 2025, outperforming the equity mixed fund index by 14.18% [65] - The Growth and Stability Portfolio achieved an annualized return of 40.56% from 2012 to 2025, exceeding the equity mixed fund index by 26.33% [70]

Quantitative Models and Construction Methods 1. Model Name: Hong Kong Stock Selection Portfolio - **Model Construction Idea**: The model aims to select stocks with both fundamental support and technical resonance from an analyst-recommended stock pool[14][15] - **Model Construction Process**: - **Step 1**: Construct an analyst-recommended stock pool based on three types of analyst recommendation events: upward earnings forecast revisions, initial analyst coverage, and analyst report titles exceeding expectations[15] - **Step 2**: Perform dual-layer selection on the analyst-recommended stock pool using fundamental and technical dimensions to select stocks with both fundamental support and technical resonance[15] - **Step 3**: The backtest period for the Hong Kong Stock Selection Portfolio is from January 1, 2010, to December 31, 2025. Considering transaction costs in a fully invested state, the portfolio's annualized return is 19.08%, with an excess return of 18.06% relative to the Hang Seng Index[15] - **Model Evaluation**: The model demonstrates a strong performance with significant excess returns over the Hang Seng Index, indicating its effectiveness in stock selection[15] Model Backtest Results - **Hong Kong Stock Selection Portfolio**: - **Annualized Return**: 19.08%[15] - **Excess Return**: 18.06% relative to the Hang Seng Index[15] - **Information Ratio (IR)**: 1.19[20] - **Tracking Error**: 14.60%[20] - **Maximum Drawdown**: 23.73%[20] - **Return-to-Drawdown Ratio**: 0.76[20] Quantitative Factors and Construction Methods 1. Factor Name: Stable New High Stocks - **Factor Construction Idea**: The factor aims to identify stocks that have recently reached new highs and exhibit stable price paths, leveraging the momentum and trend-following strategies that are particularly effective in the Hong Kong market[21] - **Factor Construction Process**: - **Step 1**: Calculate the 250-day new high distance using the formula: $$ 250 \text{ day new high distance} = 1 - \frac{Close_t}{\text{ts\_max(Close, 250)}} $$ where $Close_t$ is the latest closing price, and $\text{ts\_max(Close, 250)}$ is the maximum closing price over the past 250 trading days[23] - **Step 2**: Screen stocks that have reached a 250-day new high in the past 20 trading days based on analyst attention, relative stock strength, price path stability, and new high continuity[23] - **Step 3**: Select stocks with the following criteria: - Analyst attention: At least 5 buy or hold ratings in the past 6 months - Relative stock strength: Top 20% in terms of price change over the past 250 days - Price path stability: Top 50% based on price displacement ratio and 250-day new high distance over the past 120 days - Trend continuity: Top 50 stocks based on the 250-day new high distance over the past 5 days[24] - **Factor Evaluation**: The factor effectively captures stocks with strong momentum and stable price paths, which are likely to continue their upward trends[21][23] Factor Backtest Results - **Stable New High Stocks**: - **Example Stocks**: J&T Express-W, China Eastern Airlines, Youran Dairy, Hansoh Pharmaceutical, China XLX Fertilizer, etc.[23][29] - **Sector Distribution**: Most new high stocks are in the cyclical sector, followed by finance, technology, consumer, manufacturing, and healthcare sectors[23][29]

Quantitative Models and Factor Analysis Quantitative Models and Construction Methods Model Name: Guosen JinGong Index Enhanced Portfolio - **Model Construction Idea**: The model aims to outperform its respective benchmarks by constructing enhanced portfolios based on multiple factors[11][12] - **Model Construction Process**: 1. **Return Prediction**: Predicting the returns of stocks within the benchmark index 2. **Risk Control**: Implementing risk control measures to manage the portfolio's risk exposure 3. **Portfolio Optimization**: Optimizing the portfolio to maximize returns while adhering to risk constraints[12] - **Model Evaluation**: The model is designed to consistently outperform its benchmarks by leveraging multiple factors[11][12] Model Backtesting Results - **Guosen JinGong Index Enhanced Portfolio**: - **CSI 300 Index Enhanced Portfolio**: Weekly excess return 0.44%, annual excess return 0.44%[5][14] - **CSI 500 Index Enhanced Portfolio**: Weekly excess return -1.80%, annual excess return -1.80%[5][14] - **CSI 1000 Index Enhanced Portfolio**: Weekly excess return -2.20%, annual excess return -2.20%[5][14] - **CSI A500 Index Enhanced Portfolio**: Weekly excess return 0.61%, annual excess return 0.61%[5][14] Quantitative Factors and Construction Methods Factor Name: Single Factor MFE (Maximized Factor Exposure) Portfolio - **Factor Construction Idea**: The factor aims to maximize the exposure to a single factor while controlling for various constraints such as industry exposure, style exposure, and stock weight deviations[40][41] - **Factor Construction Process**: 1. **Optimization Model**: The optimization model is formulated as follows: $$ \begin{array}{ll} \text{max} & f^{T} w \\ \text{s.t.} & s_{l} \leq X(w - w_{b}) \leq s_{h} \\ & h_{l} \leq H(w - w_{b}) \leq h_{h} \\ & w_{l} \leq w - w_{b} \leq w_{h} \\ & b_{l} \leq B_{b} w \leq b_{h} \\ & \mathbf{0} \leq w \leq l \\ & \mathbf{1}^{T} w = 1 \end{array} $$ where \( f \) represents the factor values, \( w \) is the stock weight vector, and the constraints include style exposure, industry exposure, stock weight deviations, and component stock weight limits[40][41] 2. **Constraints**: The constraints include: - **Style Exposure**: \( X \) is the factor exposure matrix, \( w_{b} \) is the benchmark weight vector, \( s_{l} \) and \( s_{h} \) are the lower and upper bounds for style exposure[41] - **Industry Exposure**: \( H \) is the industry exposure matrix, \( h_{l} \) and \( h_{h} \) are the lower and upper bounds for industry exposure[41] - **Stock Weight Deviations**: \( w_{l} \) and \( w_{h} \) are the lower and upper bounds for stock weight deviations[41] - **Component Stock Weight Limits**: \( B_{b} \) is the 0-1 vector indicating whether a stock is a benchmark component, \( b_{l} \) and \( b_{h} \) are the lower and upper bounds for component stock weights[41] - **No Short Selling**: The weights are non-negative and sum to 1[41] 3. **Portfolio Construction**: The MFE portfolio is constructed by maximizing the factor exposure while adhering to the constraints[42][44] - **Factor Evaluation**: The MFE portfolio is used to test the effectiveness of single factors under realistic constraints, making it more likely to reflect the true predictive power of the factors in the final portfolio[40][41] Factor Backtesting Results - **CSI 300 Index**: - **Best Performing Factors (Weekly)**: Three-month institutional coverage (0.86%), DELTAROA (0.61%), DELTAROE (0.52%)[19] - **Worst Performing Factors (Weekly)**: Expected net profit QoQ (-0.78%), one-year momentum (-0.45%), idiosyncratic volatility (-0.42%)[19] - **CSI 500 Index**: - **Best Performing Factors (Weekly)**: Single-quarter net profit YoY growth (0.06%), expected net profit QoQ (0.33%), idiosyncratic volatility (0.22%)[21] - **Worst Performing Factors (Weekly)**: One-month volatility (-2.47%), EPTTM (-3.56%), single-quarter ROE (-0.67%)[21] - **CSI 1000 Index**: - **Best Performing Factors (Weekly)**: One-year momentum (1.94%), single-quarter revenue YoY growth (1.31%), standardized unexpected income (0.92%)[23] - **Worst Performing Factors (Weekly)**: EPTTM (-3.56%), dividend yield (-3.27%), expected EPTTM (-3.22%)[23] - **CSI A500 Index**: - **Best Performing Factors (Weekly)**: Single-quarter net profit YoY growth (1.14%), DELTAROE (0.88%), single-quarter operating profit YoY growth (0.70%)[25] - **Worst Performing Factors (Weekly)**: EPTTM (-1.29%), one-month volatility (-1.22%), three-month volatility (-1.09%)[25] - **Public Fund Heavy Index**: - **Best Performing Factors (Weekly)**: Single-quarter net profit YoY growth (1.14%), expected net profit QoQ (0.88%), three-month reversal (0.29%)[27] - **Worst Performing Factors (Weekly)**: Expected EPTTM (-0.74%), EPTTM (-1.29%), one-month volatility (-1.22%)[27]

- The report introduces a quantitative model named "250-day new high distance" to track market trends and identify investment hotspots. The model is based on momentum and trend-following strategies, emphasizing the effectiveness of monitoring stocks near their 52-week high prices[11][19][20] - The construction process of the "250-day new high distance" model is as follows: Formula: $ 250\text{-day new high distance} = 1 - \frac{Close_t}{ts\_max(Close, 250)} $ Explanation: - $ Close_t $ represents the latest closing price - $ ts\_max(Close, 250) $ represents the maximum closing price over the past 250 trading days If the latest closing price reaches a new high, the distance equals 0; otherwise, it is a positive value indicating the degree of price fallback[11] - The report evaluates the model positively, highlighting its ability to capture market trends and identify leading stocks in various sectors. It references studies by George (2004), William O'Neil, and Mark Minervini, which support the effectiveness of tracking stocks near their high prices[11][19] - The model's testing results show that as of January 9, 2026, major indices such as the Shanghai Composite Index, Shenzhen Component Index, and CSI 500 have a "250-day new high distance" of 0.00%, indicating they are at their peak levels. Other indices like CSI 300 and ChiNext have distances of 0.66% and 0.06%, respectively[12][13][33] - A quantitative factor named "Stable New High Stocks" is constructed to identify stocks with smooth price paths and consistent momentum. The factor incorporates analyst attention, relative price strength, price path smoothness, and sustained new high performance[26][28] - The construction process of the "Stable New High Stocks" factor includes: - Analyst attention: At least five buy or overweight ratings in the past three months - Relative price strength: Top 20% in 250-day price change - Price path smoothness: Evaluated using metrics like price displacement ratio - Sustained new high performance: Average "250-day new high distance" over the past 120 days and the last five days[26][28] - The factor is positively evaluated for its ability to capture stocks with strong and consistent momentum, supported by studies on smooth price paths and investor underreaction to gradual price changes[26][28] - Testing results for the "Stable New High Stocks" factor show that 50 stocks were selected, with the highest representation in cyclical and technology sectors. Notable stocks include Yuanjie Technology, Yaxiang Integration, and Xinwei Communication[29][34]

证券研究报告 | 2026年01月09日 热点追踪周报 由创新高个股看市场投资热点（第 226 期） 乘势而起：市场新高趋势追踪：截至 2026 年 1 月 9 日，上证指数、深证 成指、沪深 300、中证 500、中证 1000、中证 2000、创业板指、科创 50 指数 250 日新高距离分别为 0.00%、0.00%、0.66%、0.00%、0.00%、0.00%、 0.06%、4.10%。中信一级行业指数中家电、国防军工、有色金属、传媒、 电子行业指数距离 250 日新高较近，食品饮料、银行、医药、房地产、 电力及公用事业行业指数距离 250 日新高较远。概念指数中，新能源汽 车、华为平台、互联网、金属非金属、电子设备和仪器、半导体、工程 机械等概念指数距离 250 日新高较近。 见微知著：利用创新高个股进行市场监测：截至 2026 年 1 月 9 日，共 911 只股票在过去 20 个交易日间创出 250 日新高。其中创新高个股数量最多的 是机械、电子、基础化工行业，创新高个股数量占比最高的是国防军工、有 色金属、石油石化行业。按照板块分布来看，本周制造、科技板块创新高股 票数量最多；按照指数分布来 ...