宏观研究 证 券 研 究 报 告 4、价格：金铜油价齐升，碳酸锂价格继续大涨。COMEX 黄金收于 4473 美金 /盎司，上涨 3.6%；LME 三个月铜价收于 12990 美元/吨，上涨 3.8%；美油收 于 59.1 美元/桶，上涨 3.1%，布油收于 63.3 美元/桶，上涨 4.3%。碳酸锂连续 合约收盘价上涨 15.6%。 （二）景气向下 1、地产销售：商品房住宅销售降幅扩大。我们统计的 67 个城市，1 月前 10 日，商品房成交面积同比为-43%。去年 12 月同比为-24%。 2、土地溢价率：继续回落。截至 1 月 4 日当周，百城土地溢价率平均为 0.45%。 去年 12 月为 1.64%，11 月为 2.7%。 【每周经济观察】 华创宏观 WEI 指数回升 ❖ 每周经济观察： （一）景气向上 1、华创宏观 WEI 指数上行。截至 1 月 4 日，指数为 6.05%，较前一周上行 0.46 个点。 2、出行数据表现偏强。1 月第一周，26 城地铁客运量同比+6%，环比上月- 1.2%。1 月前 10 日，国内航班执行数日均为 1.24 万架次，同比-0.6%，环比去 年 12 月+0.9% ...

宏观研究 证 券 研 究 报 告 【每周经济观察】 美国 10 月贸易逆差缩窄至 2009 年中以来最 低——海外周报第 122 期 ❖ 核心观点： 1、重要数据回顾：①美国 12 月 ADP 就业人数低于预期，11 月 JOLTs 职位 空缺数低于预期，10 月贸易逆差规模缩窄至 2009 年中以来最低，12 月 ISM 服务业 PMI 大幅上升，12 月 ISM 制造业 PMI 下滑，12 月季调后非农就业人 数低于市场预期，1 月密歇根大学消费者信心指数创 4 个月新高。②日本 2025 年 11 月薪资增速低于预期。③欧元区中德国和法国通胀超预期放缓。 2、美国基本面高频：①景气上行的有：地产（房贷申请数量回升）、就业（职 位空缺数回升）。②景气下行的有：WEI 指数（经济景气下行）、消费（红皮 书商业零售同比回落）、物价（大宗价格回升、美国汽油零售价回落）、就业 （ADP 周度新增就业人数回落、初请失业金人数回升、续请失业金人数回升）。 3、美国流动性高频：①美国和欧元区金融条件宽松。②离岸美元流动性：日 ❖ 一、过去一周重要数据回顾 1、美国 12 月 ADP 就业人数低于预期，11 月 JOLT ...

宏观研究 证 券 研 究 报 告 【宏观快评】2025 年 12 月非农数据点评 10W！或是美国降息的就业分水岭 主要观点 ❖ 2025 年 12 月份非农数据简述 1、新增非农低于预期，前两个月数据明显下修。新增非农就业 5 万，预期 7 万。私人部门新增非农就业 3.7 万，预期 7.5 万。10-11 月新增就业合计下修 7.6 万。就业增长集中在教育保健服务（+4.1 万，前值+5.9 万）、休闲酒店（+4.7 万，前值-0.3 万）。零售、建筑、制造业、专业和商业服务等行业就业萎缩。 2、失业率意外回落，录得 4.4%（4.375%），预期 4.5%，前值 4.5%（4.536%）。 劳动参与率从 62.46%降至 62.40%，预期 62.4%。失业率下行主要源于就业增 长和供给小幅收缩，前者影响约 0.14 个百分点，后者影响约 0.03 个百分点。 3、时薪增速符合预期，但周工时下滑。私人行业时薪环比 0.3%，预期 0.3%， 同比增速 3.8%，预期 3.6%。周工时从 34.3 小时降至 34.2 小时，仍处于 2015 年以来的低位水平。周薪环比持平，并未增长（时薪是周薪和周工时的倒 ...

华创交运|低空经济周报（第 61 期） 国家安全视角再论无人机"攻与防"；"航空 推荐（维持） 强国"系列建议关注商发产业链 交通运输 2026 年 1 月 11 日 华创证券研究所 行业研究 证 券 研 究 报 告 证券分析师：卢浩敏 邮箱：luhaomin@hcyjs.com 执业编号：S0360524090001 证券分析师：李清影 证券分析师：吴一凡 邮箱：wuyifan@hcyjs.com 执业编号：S0360516090002 证券分析师：梁婉怡 邮箱：liangwanyi@hcyjs.com 执业编号：S0360523080001 证券分析师：吴晨玥 邮箱：wuchenyue@hcyjs.com 执业编号：S0360523070001 证券分析师：霍鹏浩 邮箱：huopenghao@hcyjs.com 执业编号：S0360524030001 邮箱：liqingying@hcyjs.com 执业编号：S0360525080004 联系人：刘邢雨 邮箱：liuxingyu@hcyjs.com 行业基本数据 | | | 占比% | | --- | --- | --- | | 股票家数(只) | 121 ...

Investment Rating - The report maintains a "Recommended" rating for the mechanical industry, with a focus on AI equipment and consumables, as well as engineering machinery [1]. Core Insights - The mechanical industry is expected to benefit from the acceleration of AI applications, particularly in high-performance servers and GPU demand, driven by the rapid iteration of AI models and smart hardware [7]. - The excavator market is projected to exceed expectations in both domestic and international sales, with a forecasted 17% year-on-year growth in 2025, supported by government policies and infrastructure projects [7]. - The report emphasizes the potential for a new recovery cycle in the equipment industry, driven by monetary and fiscal policy support, and suggests focusing on key companies across various segments [7]. Summary by Sections Key Company Earnings Forecast, Valuation, and Investment Ratings - Companies such as 汇川技术 (Inovance Technology), 法兰泰克 (Falan Tech), and 信捷电气 (Xinjie Electric) are rated as "Strong Buy" with projected EPS growth and favorable PE ratios [2][8]. - For example, 汇川技术 is expected to have an EPS of 2.11元 in 2025, with a PE ratio of 37.13, indicating strong growth potential [2]. Industry and Company Investment Views - The report highlights the AI equipment and consumables sector as a key area for investment, with significant growth expected in the PCB market driven by AI infrastructure needs [9]. - The engineering machinery sector is also highlighted, with companies like 三一重工 (Sany Heavy Industry) and 徐工机械 (XCMG) expected to benefit from increased domestic demand and international market recovery [7][9]. Key Data Tracking - The report provides macroeconomic data indicating a total market capitalization of 70,956.73 billion yuan for the mechanical industry, with 636 listed companies [4]. - The mechanical sector has shown strong performance, with a 5.7% increase in the sector index over the past week, outperforming major indices [11][14].

Investment Rating - The report maintains a "Buy" rating for the non-ferrous metals sector, highlighting the initiation of a spring rally with significant price increases in metals [2]. Core Views - The spring rally is believed to have started, with aluminum prices showing strong elasticity. As of January 9, the SHFE aluminum closing price was 24,385 CNY/ton, a 6.4% increase from December 31, 2025. The report anticipates that aluminum prices may rise further due to rigid supply constraints and increasing demand in new sectors [3][4]. - The report emphasizes the positive outlook for the electrolytic aluminum sector, predicting average profits to exceed 7,500 CNY/ton, supported by improved cash flow and stable profitability among companies [4]. - A strike at the Mantoverde copper mine in Chile could impact copper production, potentially exacerbating supply tightness in 2026 [5]. Summary by Sections Industrial Metals - **Aluminum Market**: The report notes a significant increase in aluminum prices and a rise in profits, driven by supply constraints and new demand areas. The global aluminum inventory remains low, providing strong support for prices [3]. - **Copper Market**: The report highlights a rise in copper inventories and recommends several companies in the copper sector, including Zijin Mining and Western Mining [6]. New Energy Metals and Minor Metals - **Cobalt Market**: The report indicates that cobalt exports from the Democratic Republic of Congo are delayed, leading to a potential price increase. The average price of electrolytic cobalt rose to 460,000 CNY/ton, a 1.1% increase from December 31, 2025 [7][12]. - **Company Performance**: Huayou Cobalt's 2025 earnings forecast exceeds market expectations, with a projected net profit increase of 40.8% to 55.2% year-on-year [14]. Industry Data - **Market Performance**: The non-ferrous metals sector has shown strong absolute and relative performance over the past year, with a 110.2% increase over 12 months [9]. - **Stock Market Data**: The total market capitalization of the sector is approximately 457.86 billion CNY, with 126 listed companies [8].

金融工程 证 券 研 究 报 告 市场情绪监控周报（20260105-20260109） 本周宽基热度变化方面：热度变化率最大的为中证 500，相比上周提高 17.56%， 最小的为中证 2000，相比上周降低 8.81%。 本周申万行业热度变化方面，一级行业中热度变化率正向变化前 5 的一级行 业分别为传媒、石油石化、煤炭、国防军工、计算机，负向变化前 5 的一级行 业分别为综合、农林牧渔、食品饮料、交通运输、商贸零售；申万二级行业中， 热度正向变化率最大的 5 个行业是广告营销、油服工程、风电设备、专业服 务、地面兵装Ⅱ。 本周概念热度变化最大的 5 个概念为脑机接口、高压氧舱、血氧仪、细胞免疫 治疗、小红书概念。 ❖ 本周市场估值跟踪 本周宽基和行业估值：沪深 300、中证 500、中证 1000 的滚动 5 年历史分位数 分别为 92%、100%、100%。 申万一级行业中，从 2015 年开始回溯，当前估值处于历史分位数 80%以上的 一级行业有：电子、电力设备、国防军工、轻工制造、商贸零售、钢铁、环保、 计算机、建筑材料、银行、医药生物、基础化工、煤炭；位于估值历史 20%以 下的有食品饮料、非银 ...

Quantitative Models and Construction Methods 1. Model Name: Volume Model - **Construction Idea**: The model uses trading volume data to predict market trends[1][13] - **Construction Process**: The model analyzes the trading volume of various broad-based indices to generate buy or sell signals[1][13] - **Evaluation**: The model is effective in capturing short-term market movements[1][13] 2. Model Name: Feature Dragon Tiger List Institutional Model - **Construction Idea**: This model uses institutional trading data from the Dragon Tiger List to predict market trends[1][13] - **Construction Process**: The model analyzes the trading activities of institutions listed on the Dragon Tiger List to generate buy or sell signals[1][13] - **Evaluation**: The model is useful for understanding institutional trading behavior and its impact on the market[1][13] 3. Model Name: Feature Volume Model - **Construction Idea**: This model uses specific volume characteristics to predict market trends[1][13] - **Construction Process**: The model analyzes specific volume patterns to generate buy or sell signals[1][13] - **Evaluation**: The model is effective in identifying significant volume changes that precede market movements[1][13] 4. Model Name: Intelligent Algorithm CSI 300 Model - **Construction Idea**: This model uses intelligent algorithms to predict the CSI 300 index trends[1][13] - **Construction Process**: The model employs machine learning algorithms to analyze historical data and generate buy or sell signals for the CSI 300 index[1][13] - **Evaluation**: The model leverages advanced algorithms to improve prediction accuracy[1][13] 5. Model Name: Intelligent Algorithm CSI 500 Model - **Construction Idea**: This model uses intelligent algorithms to predict the CSI 500 index trends[1][13] - **Construction Process**: The model employs machine learning algorithms to analyze historical data and generate buy or sell signals for the CSI 500 index[1][13] - **Evaluation**: The model leverages advanced algorithms to improve prediction accuracy[1][13] 6. Model Name: Limit Up and Down Model - **Construction Idea**: This model uses the occurrence of limit up and down events to predict market trends[1][13] - **Construction Process**: The model analyzes the frequency and context of limit up and down events to generate buy or sell signals[1][13] - **Evaluation**: The model is effective in capturing extreme market movements[1][13] 7. Model Name: Up and Down Return Difference Model - **Construction Idea**: This model uses the difference between upward and downward returns to predict market trends[1][13] - **Construction Process**: The model calculates the difference between upward and downward returns to generate buy or sell signals[1][13] - **Evaluation**: The model provides insights into market momentum and potential reversals[1][13] 8. Model Name: Calendar Effect Model - **Construction Idea**: This model uses calendar-based patterns to predict market trends[1][13] - **Construction Process**: The model analyzes historical data to identify recurring calendar-based patterns and generate buy or sell signals[1][13] - **Evaluation**: The model is useful for identifying seasonal trends in the market[1][13] 9. Model Name: Long-term Momentum Model - **Construction Idea**: This model uses long-term momentum to predict market trends[1][14] - **Construction Process**: The model analyzes long-term price momentum to generate buy or sell signals[1][14] - **Evaluation**: The model is effective in capturing long-term market trends[1][14] 10. Model Name: A-Share Comprehensive Weapon V3 Model - **Construction Idea**: This model combines multiple factors to predict market trends[1][15] - **Construction Process**: The model integrates various indicators and models to generate a comprehensive buy or sell signal[1][15] - **Evaluation**: The model provides a holistic view of the market by combining multiple factors[1][15] 11. Model Name: A-Share Comprehensive Guozheng 2000 Model - **Construction Idea**: This model combines multiple factors to predict the Guozheng 2000 index trends[1][15] - **Construction Process**: The model integrates various indicators and models to generate a comprehensive buy or sell signal for the Guozheng 2000 index[1][15] - **Evaluation**: The model provides a holistic view of the market by combining multiple factors[1][15] 12. Model Name: Turnover Rate Inverse Volatility Model - **Construction Idea**: This model uses the inverse relationship between turnover rate and volatility to predict market trends[1][16] - **Construction Process**: The model analyzes the turnover rate and its inverse relationship with volatility to generate buy or sell signals[1][16] - **Evaluation**: The model is effective in identifying periods of high market uncertainty[1][16] Model Backtesting Results 1. Volume Model - **Indicator Value**: All broad-based indices are bullish[1][13] 2. Feature Dragon Tiger List Institutional Model - **Indicator Value**: Bullish[1][13] 3. Feature Volume Model - **Indicator Value**: Bullish[1][13] 4. Intelligent Algorithm CSI 300 Model - **Indicator Value**: Bullish[1][13] 5. Intelligent Algorithm CSI 500 Model - **Indicator Value**: Bullish[1][13] 6. Limit Up and Down Model - **Indicator Value**: Bullish[1][13] 7. Up and Down Return Difference Model - **Indicator Value**: All broad-based indices are bullish[1][13] 8. Calendar Effect Model - **Indicator Value**: Neutral[1][13] 9. Long-term Momentum Model - **Indicator Value**: Some broad-based indices are bullish[1][14] 10. A-Share Comprehensive Weapon V3 Model - **Indicator Value**: Bullish[1][15] 11. A-Share Comprehensive Guozheng 2000 Model - **Indicator Value**: Bullish[1][15] 12. Turnover Rate Inverse Volatility Model - **Indicator Value**: Bearish[1][16]

宏观研究 证 券 研 究 报 告 【宏观快评】2025 年 12 月通胀数据点评 物价：回顾 2025，展望 2026 主要观点 ❖ 回顾 2025 物价之整体形势：低位筑底 1、2025 年 12 月物价继续改善。CPI 同比从 0.7%升至 0.8%，预期 0.75%；核 心 CPI 同比高位持平于 1.2%；PPI 同比从-2.2%收窄至-1.9%，预期-2%。 2、季度来看，2025 年 CPI 同比前低后高，核心 CPI 同比逐季上行，PPI 同 比 V 形走势。2025 年 4 季度，CPI 食品、核心 CPI 和 PPI 的同比均为年内高 点，预计 4 季度 GDP 平减指数约-0.4%，1-3 季度分别为-0.8%、-1.2%、-1%。 3、年度来看，结合后续展望，2025 年物价或是低位筑底。CPI 同比 0%，小幅 低于 2023-24 年的 0.2%。核心 CPI 同比 0.8%，高于 2024 年的 0.5%。PPI 同 比-2.6%，低于 2024 年的-2.2%。GDP 平减指数约-0.9%，2024 年为-0.7%。 ❖ 回顾 2025 物价之 CPI：从普遍性走弱到结构性好转 ...

金融工程 证 券 研 究 报 告 【专题报告】 四季度指数涨跌互现，市场或震荡向上—— 2025 年四季度策略总结与未来行情预判 ❖ 摘要 2025 年第四季度已经过去，不同指数涨跌互现 ，其中创成长季度上涨 5.03%， 上证指数涨幅 2.22%。 从行业表现来看，四季度大部分中信一级行业正收益，其中石油石化上涨 16.97%，国防军工上涨 16.74%。 从择时收益上讲，2025 年第四季度择时模型总体表现能获取绝对正收益，虽 然大多数模型今年表现较难超越基准本身，但是在第四个季度的表现却可圈可 点。 2025 年四季度绝对收益表现优秀模型为上下行收益差模型、成交额倒波幅模 型、综合兵器 V3 模型、动量摆动模型、推波助澜 V3 模型、低波之刃模型、 沪深 300 指数智能择时模型。 最新择时信号： 短期：成交量模型所有宽基指数看多。低波动模型中性。智能 300 模型看多， 智能 500 模型看多。特征龙虎榜机构模型看多。特征成交量模型看多。 中期：涨跌停模型看多。上下行收益差模型所有宽基指数看多。月历效应模型 中性。 长期：动量模型部分宽基指数看多。 综合：综合兵器 V3 模型看多。综合国证 2000 ...