策略研究 | 证券研究报告 — 总量周报 2026 年 2 月 8 日 策略周报 "春躁"调整期，静待 AI 催化 "春躁"行情进入节奏调整期，节后科技成长有望重回主线，AI 应用或有望 迎来触底反弹。 中银国际证券股份有限公司 具备证券投资咨询业务资格 策略研究 证券分析师：王君 (8610)66229061 jun.wang@bocichina.com 证券投资咨询业务证书编号：S1300519060003 证券分析师：徐沛东 (8621)20328702 peidong.xu@bocichina.com 证券投资咨询业务证书编号：S1300518020001 证券分析师：郭晓希 (8610)66229019 xiaoxi.guo@bocichina.com 证券投资咨询业务证书编号：S1300521110001 证券分析师：徐亚 (8621)20328506 ya.xu@bocichina.com 证券投资咨询业务证书编号：S1300521070003 证券分析师：高天然 tianran.gao@bocichina.com 证券投资咨询业务证书编号：S1300522100001 ◼ 海外商品价格波动，" ...

金融工程| 证券研究报告 —周报 2026 年 2 月 8 日 中银量化大类资产跟踪 贵金属巨震，宽松流动性持续利好微盘风格 股票市场概览 ◼ 本周 A 股市场整体下跌，港股市场整体下跌，美股市场普遍下跌，其 他海外权益市场普遍上涨。 ◼ 本周中国商品市场整体下跌，美国商品市场整体下跌。 A 股风格与拥挤度 成长 vs 红利：相对拥挤度及超额净值近期处于历史较高位置，需注 意成长风格的配置风险。 风险提示 小盘vs大盘：相对拥挤度及超额净值均未处于历史高位，小盘风格当 前具有较高的配置性价比。 微盘股 vs 中证 800：相对拥挤度及超额净值持续处于历史高位，需注 意微盘股风格的配置风险。 A 股行情跟踪 A 股估值与股债性价比 A 股资金面 汇率市场 ◼ 近一周在岸人民币较美元升值，离岸人民币较美元升值。 商品市场 ◼ 量化模型因市场剧烈变动失效。 中银国际证券股份有限公司 具备证券投资咨询业务资格 金融工程 证券分析师: 郭策 (8610) 66229081 ce.guo@bocichina.com 证券投资咨询业务证书编号：S1300522080002 证券分析师:宋坤笛 (8610) 83949524 ...

马斯克团队计划光伏扩产，钠电应用加速 电力设备 | 证券研究报告 — 行业周报 2026 年 2 月 8 日 强于大市 电力设备与新能源行业 2 月 第 1 周周报 新能源汽车方面，我们预计 2026 年全球新能源汽车销量有望保持较快增长，带动电 池和材料需求增长。动力电池方面，近期材料价格波动较大，重点关注产业链顺价 情况。新技术方面，固态电池迈向工程化验证关键期，关注相关材料和设备企业验 证进展。光伏方面，"反内卷"、"太空光伏"是 2026 年光伏投资双主线，马斯克团队 开始针对国内厂商进行调研，光伏设备景气度进一步提升，短期设备订单具备增量 订单，长期看好国内优势材料兑现海外增量利润。国内方面，国家工信部再次点名 "反内卷"，由于白银价格上涨，电池片环节格局正在优化，贱金属导入速度有望加 快。国内已出现高功率组件需求，下游电池组件依赖提效进行市场化出清，且组件 高功率化有望推动组件单价提升，组件厂涨价诉求强，建议关注行业格局较好的胶 膜、硅料、电池组件、钙钛矿、BC 方向。风电方面，李强总理表示我国愿同上合组 织各方扎实推进未来 5 年新增"千万千瓦光伏"和"千万千瓦风电"项目，风电需求有 望保持持续 ...

基础化工 | 证券研究报告 — 行业周报 2026 年 2 月 8 日 强于大市 化工行业周报 20260208 国际油价回调，己内酰胺、维生素 E 价格上涨 二月份建议关注：1、低估值行业龙头公司；2、"反内卷"对相关子行业供给端影响；3、下游需求旺 盛，自主可控日益关键背景下的电子材料公司。 行业动态 投资建议 ◼ 截至 2 月 8 日，SW 基础化工市盈率（TTM 剔除负值）为 28.57 倍，处在历史（2002 年至今） 82.89%分位数；市净率为 2.58 倍，处在历史 73.42%分位数。SW 石油石化市盈率（TTM 剔除负 值）为 15.16 倍，处在历史（2002 年至今）46.10%分位数；市净率为 1.47 倍，处在历史 50.93% 分位数。展望 2026 年，本轮行业扩产已近尾声，"反内卷"等措施有望催化行业盈利底部修复， 同时新材料受益于下游需求的快速发展，有望开启新一轮高成长，二月份建议关注：1、低估值行 业龙头公司；2、"反内卷"对相关子行业供给端影响；3、下游需求旺盛，自主可控日益关键背 景下的电子材料公司。中长期推荐投资主线：1、传统化工龙头经营韧性凸显，布局新材料等领域， ...

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金融工程 | 证券研究报告 — 周报 2026 年 2 月 6 日 中银量化多策略行业轮动 周报 – 20260205 当前（2026年 2月 5日）中银多策略行业配置系统仓位：通信（17.0%）、 基础化工（15.1%）、电力设备及新能源（10.7%）、煤炭（10.2%）、医 药（7.9%）、建材（7.0%）、电子（4.2%）、计算机（4.2%）、非银行 金融（4.0%）、消费者服务（3.8%）、综合（3.8%）、银行（3.0%）、 家电（3.0%）、建筑（3.0%）、石油石化（3.0%）。 S5 传统多因子打分策略（季度）超额收益为-2.7% 相关研究报告 《中银证券量化行业轮动系列（七）：如何把 握市场"未证伪情绪"构建行业动量策略》 20220917 《中银证券量化行业轮动系列（八）："估值泡 沫保护"的高景气行业轮动策略》20221018 《中银证券宏观基本面行业轮动新框架：对传 统自上而下资产配置困境的破局》20230518 《中银证券量化行业轮动系列（九）：长期反 转-中期动量-低拥挤"行业轮动策略》20240914 《中银证券量化行业轮动系列（十）：如何基 于资金流构建行业轮动策略？》2025 ...

策略研究 | 证券研究报告 — 总量点评 2026 年 2 月 6 日 策略点评 2025 年 A 股业绩预告 整体景气延续修复，扩散度上行，景气行业趋势延续。 中银国际证券股份有限公司 具备证券投资咨询业务资格 策略研究 证券分析师：王君 (8610)66229061 jun.wang@bocichina.com 证券投资咨询业务证书编号：S1300519060003 证券分析师：徐亚 （8621）20328506 ya.xu@bocichina.com 证券投资咨询业务证书编号：S1300521070003 ◼ 截至 2026 年 2 月 5 日，全 A 共有 3 家公司披露 2025 年年报，84 家公司 发布业绩快报，2950 家公司发布业绩预告，合计披露 2025 年经营数据的 公司数为 3037 家，整体披露率 58.8%。 ◼ 样本公司 2025 年整体法口径下，盈利增速为 36.9%，较 2025Q3 的 14.3% 有所上行。中位数法口径下，盈利增速为 15.7%，较 2025Q3 的 3.2%有所 上行。 ◼ 样本公司（非金融）整体法口径下，2025 年整体法盈利增速为 247.0%， 较 ...

Core Insights - The report emphasizes that the recent pullback in the AI industry is a necessary phase in the deep integration of AI technology into various sectors, rather than a fundamental threat to the industry's future [1][6] - It suggests that the current market concerns regarding the uncertainty of AI application business models and hardware demand are part of the industry's evolution, and that this pullback presents investment opportunities in AI applications, cloud services, and storage [2][6] Market Trends - Since mid-January 2026, the AI industry chain has experienced a continuous pullback, exacerbated by several events in early February, including Microsoft's financial report revealing dual concerns about growth dependency and investment returns [2][3] - Microsoft's Q2 2026 financial report indicated a slowdown in Azure cloud computing growth and projected capital expenditures exceeding $100 billion, with approximately 45% of its cloud business backlog dependent on OpenAI [3] - Concerns were also raised regarding NVIDIA's investment stance on OpenAI, with CEO Jensen Huang indicating a cautious approach to investment, despite previous indications of a potential $100 billion investment [4] Business Model Uncertainty - The report identifies dual uncertainties in the market: the uncertainty of AI application business models and the uncertainty of real demand [5][6] - It highlights that traditional SaaS companies may face challenges as enterprises consider building their own AI tools, potentially undermining SaaS profitability [5][6] - The report argues that the market's valuation logic is shifting from paying premiums for future potential to assessing current realities and investment returns [5] Long-term Outlook - The report posits that the concerns regarding business model and demand uncertainties are part of the necessary evolution towards deeper integration of AI technology, rather than a fundamental threat to the industry's prospects [6] - It suggests that traditional application vendors can leverage their industry knowledge and data advantages to build new barriers in the AI era, and that early movers may see valuation increases [6][7] - The demand for hardware is expected to grow in tandem with the maturity of software applications, as AI applications transition from "technology demonstrations" to "production tools" [7]

Core Insights - The report highlights the contradiction faced during the "14th Five-Year Plan" period, where carbon reduction pressures are increasing while the growth rate of new energy installations is slowing down. The introduction of a national capacity price policy is expected to open up space for new energy installations and support high-yield investment options for power companies during the "14th Five-Year Plan" investment intensity [5][6][9]. Group 1: Energy Storage Industry - The national capacity price policy, issued on January 30, 2026, aims to establish a mechanism that balances power supply stability, green energy transformation, and efficient resource allocation. This policy is expected to support the development of adjustable power sources and enhance the installation of new energy [7][9]. - The report estimates that the demand for energy storage will show a high growth trend, with new energy storage installations expected to reach 66.43 GW and 189.48 GWh in 2025, representing year-on-year increases of 52% and 73% respectively [8][9]. - The capacity price policy is seen as the final piece needed for energy storage development, potentially increasing project returns from approximately 6.5% to over 8% under current subsidy conditions. This is expected to stimulate investment interest from state-owned enterprises in new energy storage projects [8][9]. Group 2: Investment Recommendations - The report suggests prioritizing investments in leading companies involved in energy storage integration and upstream battery cells, recommending firms such as Sungrow Power Supply, Trina Solar, LONGi Green Energy, JinkoSolar, CATL, and Eve Energy. It also advises monitoring companies like Haisum, Sungrow Electric, Canadian Solar, and Penghui Energy [9].

Core Insights - The report introduces a macro factor assetization framework for timing strategies in government bond futures, demonstrating robust return characteristics and strong risk resistance through backtesting [1][2]. Group 1: Macro Real-Time (PIT) Indicator Library Construction - The macro factor assetization strategy utilizes real-time macro data, contrasting with traditional models that lag by 1-2 months, by employing a precise macroeconomic calendar to obtain macro data disclosure dates and times [4][19]. - The PIT macro indicators are designed across four dimensions: economic growth, inflation, monetary credit policy, and central bank open market operations, creating a macro factor library [4][19]. Group 2: Strategy Construction and Backtesting - The strategy framework consists of three main steps: macro factor construction, macro trading logic net value realization, and dynamic factor selection and combination [4][29]. - The model achieved a post-fee Sharpe ratio of approximately 1.3 and a Calmar ratio of about 1.1, indicating strong performance despite challenges in capturing significant excess returns during the bull market from 2021 to 2024 [4][29]. - The model's performance is relatively insensitive to the lag parameter n, with optimal settings found between 10 to 30 minutes, leading to a standardized approach of a 10-minute lag for all signals [4][29]. Group 3: Factor Dynamic Selection and Combination - The macro factors are categorized into four types: economic growth, inflation, monetary credit, and open market operations, with each factor's performance analyzed for effective timing signals [4][29]. - The report emphasizes the importance of dynamic factor selection to enhance model performance, utilizing momentum factor selection methods to optimize the factor pool [4][29][56]. - The empirical results indicate that the combined signals from multiple factors significantly improve timing effectiveness compared to single-factor performance [4][29][74].