Group 1 - The stock price of Naxin Micro (688052.SH) fell by 2.54%, closing at 148.16 yuan [1] - Naxin Micro was listed on the Shanghai Stock Exchange's Sci-Tech Innovation Board on April 22, 2022, with an initial public offering (IPO) of 25.266 million shares at a price of 230.00 yuan per share [1] - The company is currently in a state of share price decline, having broken its initial offering price [1] Group 2 - The total amount raised from the IPO was 5.811 billion yuan, with a net amount of 5.581 billion yuan, exceeding the originally planned fundraising by 4.831 billion yuan [1] - The original fundraising target was 750 million yuan, intended for signal chain chip development, R&D center construction, and working capital [1] - The total issuance costs for the IPO were 230 million yuan (excluding VAT), with underwriting fees amounting to 203 million yuan [1] Group 3 - On May 24, 2023, Naxin Micro announced a profit distribution plan, distributing a cash dividend of 0.8 yuan per share (tax included) and a capital reserve increase of 0.4 shares per share [1] - The total cash dividend distributed amounted to 80.8512 million yuan, with a total of 40.4256 million shares being increased [1] - Following this distribution, the total share capital of the company increased to 141.4896 million shares [1]

12月16日，上交所发布关于光大证券股份有限公司2025年面向专业投资者公开发行公司债券（第五期） （品种一）上市的公告。 依据《上海证券交易所公司债券上市规则》等规定，上交所同意光大证券股份有限公司2025年面向专业 投资者公开发行公司债券（第五期）（品种一）于2025年12月17日起在上交所上市，并采取匹配成交、 点击成交、询价成交、竞买成交、协商成交交易方式。该债券证券简称为"25光证G7"，证券代码 为"244286"。根据中国结算规则，可参与质押式回购。 来源：市场资讯 ...

Consumption - In November 2025, the year-on-year growth rate of social retail sales was 1.3%, below the expected 2.9%, marking the lowest point since February 2023[3] - The decline in consumption is attributed to last year's "trade-in" policy raising the base, and a decrease in service consumption after the long holiday[2] - The retail sales of five categories involved in the "trade-in" policy saw a decline, with home appliances and furniture experiencing negative year-on-year growth[4] Investment - From January to November 2025, fixed asset investment showed a cumulative year-on-year decrease of 2.6%, worse than the expected decrease of 2.2%[5] - In November, the year-on-year decline in fixed asset investment was -11.1%, with manufacturing investment improving slightly to -4.4%[13] - Infrastructure investment continued to decline, with narrow and broad infrastructure showing year-on-year decreases of -9.7% and -12.0%, respectively[19] Real Estate - In November 2025, the year-on-year growth rate of national commodity housing sales fell to -26.1%, down from -25.1% in October[23] - Real estate development investment saw a significant decline, with a year-on-year decrease of -31.4% in November, reaching a low level[23] - The two-year compound growth rate for commodity housing sales area improved slightly, from -11.1% in October to -7.9% in November[23]

1. Report Industry Investment Rating No relevant content provided. 2. Core View of the Report The report systematically reviews the evolution of financial time - series forecasting models and constructs a prediction model for China's 10 - year treasury bond yield using a long - short - term memory (LSTM) neural network with historical time series as the single input variable, initially exploring the application of this deep - learning model in the fixed - income quantitative field [10]. 3. Summary by Relevant Catalog 3.1 Financial Time - Series Forecasting and Neural Network Models 3.1.1 Evolution of Financial Time - Series Forecasting Models Financial time - series forecasting has gone through three main development stages: traditional econometric models, traditional machine - learning models, and deep - learning models. Traditional econometric models have clear forms and strong interpretability but struggle to depict nonlinear and complex dynamic relationships. Traditional machine - learning models can perform nonlinear fitting and automatic feature screening but need manual feature extraction. Deep - learning models can automatically extract features from raw data and capture complex long - term time - series patterns, adapting well to the complex characteristics of financial time series [11][12]. 3.1.2 Neural Network Models and LSTM Models Neural network models are machine - learning models imitating the connection structure of human brain neurons. Recurrent neural networks (RNN) and their variants, such as LSTM, are designed for processing sequence data. LSTM solves the long - term dependence problem of traditional RNN through a "gating mechanism" and memory units, enhancing robustness to irregular data and being suitable for bond yield prediction [13][18]. 3.2 Treasury Bond Yield Prediction Based on Stacked LSTM Model 3.2.1 Stacked LSTM Model Stacked LSTM connects multiple LSTM layers in sequence, having advantages in long - sequence processing and multi - dimensional feature extraction, more suitable for complex time - series forecasting in financial scenarios [23]. 3.2.2 Construction of Treasury Bond Yield Prediction Model The report uses a classic and robust architecture of three - layer stacked LSTM + Dropout regularization to build a neural network model for predicting the 10 - year treasury bond yield. It only uses the historical time series of the 10 - year treasury bond yield as a single variable for prediction. The data is from the beginning of 2021 to December 12, 2025. After data processing and sample construction, a medium - complexity LSTM neural network model with about 130,000 adjustable parameters is built. The optimal model is obtained at the 27th training iteration, with an average absolute error of 1.43BP for the test set. The predicted yield on December 19, 2025, is 1.8330%, slightly lower than 1.8396% on December 12, 2025 [2][24][30]. 3.3 Follow - up Optimization Directions - Optimize model design: Adjust and optimize the design related to time windows, data processing, network architecture, and training strategies [3][36]. - Input multi - dimensional variables: Expand input variables from a single yield sequence to multi - dimensional variables such as macro, market, and sentiment to make the model more in line with economic logic and capture more comprehensive information [3][36]. - Build hybrid models: Combine the LSTM model with traditional econometric models or other machine - learning models to build hybrid models like ARIMAX - LSTM and CNN - LSTM - ATT, enhancing prediction accuracy [3][36]. - Introduce a rolling back - testing mechanism: Use a rolling time - window back - testing mechanism to update the model dynamically and make continuous predictions, improving the model's adaptability to market changes [3][36].

Core Viewpoint - Company is rated "Buy" by Everbright Securities, highlighting its leading position in the global AMR warehouse fulfillment solutions market and strong commercialization capabilities [1] Group 1: Revenue and Market Position - Company is the global leader in AMR warehouse fulfillment solutions, with a projected revenue of 2.409 billion yuan in 2024, maintaining the number one market share for six consecutive years [1] - The global warehouse automation trend is on the rise, with approximately 80% of warehouses yet to deploy automation solutions, indicating significant market capture potential for AMR solutions [1] - The market size for AMR solutions is expected to reach 162.1 billion yuan by 2029, with a CAGR of 33.1% from 2024 to 2029 [1] Group 2: Customer Base and Service Capabilities - Company has a strong and high-retention global customer base, with 80% of revenue coming from overseas and over 66,000 robots delivered to more than 850 clients across 40 countries [2] - The customer repurchase rate has increased from 58.3% in 2022 to 74.6% in 2024, with key clients showing a repurchase rate of 84.3% [2] - Company offers a diverse range of AMR solutions tailored to various customer needs across manufacturing, e-commerce, and logistics sectors [2] Group 3: Investment in AI and Embodied Intelligence - Company plans to establish a wholly-owned subsidiary focused on embodied intelligence by July 2025, extending core technologies to new applications [3] - The company has accumulated valuable data from servicing major clients, which will support the ongoing training of embodied intelligence models [3]

（张宇生/郭磊）2025-12-14 您可点击今日推送内容的第1条查看 点击注册小程序 查看完整报告 特别申明： 本订阅号中所涉及的证券研究信息由光大证券研究所编写，仅面向光大证券专业投资者客户，用作新媒体形势下研究 信息和研究观点的沟通交流。非光大证券专业投资者客户，请勿订阅、接收或使用本订阅号中的任何信息。本订阅号 难以设置访问权限，若给您造成不便，敬请谅解。光大证券研究所不会因关注、收到或阅读本订阅号推送内容而视相 关人员为光大证券的客户。 今 日 聚 焦 【策略】新一轮政策部署护航，A股跨年行情可期——策略周专题（2025年12月第2期） 新一轮政策部署护航，A股跨年行情可期。一方面，未来国内经济政策有望持续发力，经济增长有望保持在合 理区间，进一步夯实资本市场繁荣发展的基础；另一方面，政策红利释放，有望提振市场信心，进一步吸引各 类资金积极流入；此外，历史来看，"十三五"和"十四五"开局之年A股市场均有不错的表现，历史上开局之年 的积极表现有望在2026年得到延续。 您可点击今日推送内容的第3条查看 【房地产】11月核心15城二手房成交面积环比+15%——光大核心城市房地产销售跟踪（2025年11月 ...

Core Viewpoint - The report provides a comprehensive analysis of market performance, highlighting the performance of various factors and strategies across different stock pools, indicating potential investment opportunities and trends in the market [4][5][6][7][8][9][10]. Factor Performance - In the large-cap factor performance for the week of December 8-12, 2025, the size factor, beta factor, and non-linear market cap factor achieved positive returns of 1.18%, 0.91%, and 0.82% respectively, while the BP factor and liquidity factor recorded negative returns of -0.55% and -0.38% [4]. - In the CSI 300 stock pool, the best-performing factors included total asset growth rate (2.05%), quarterly ROA (1.71%), and turnover rate relative volatility (1.59%), while the worst-performing factors were logarithmic market cap (-1.00%), downside volatility ratio (-1.10%), and large net inflow (-1.14%) [5]. - In the CSI 500 stock pool, the top factors were quarterly EPS (1.61%), total asset growth rate (1.39%), and momentum spring factor (1.22%), with the worst being price-to-sales ratio TTM inverse (-2.49%), downside volatility ratio (-2.55%), and price-to-book ratio (-3.06%) [5]. - In the liquidity 1500 stock pool, the best factors were total asset growth rate (2.25%), quarterly revenue growth rate (2.05%), and quarterly ROA year-on-year (1.92%), while the worst were price-to-earnings ratio (-0.90%), downside volatility ratio (-0.95%), and price-to-book ratio (-0.97%) [5]. Industry Factor Performance - The net asset growth rate factor performed well in the telecommunications, comprehensive, and coal industries, while the net profit growth rate factor excelled in the telecommunications sector [6]. - The earnings per share factor showed strong performance in the telecommunications industry, and the residual volatility factor performed well in telecommunications and commercial trade sectors [6]. Strategy Performance - The PB-ROE-50 combination achieved significant excess returns across stock pools, with the CSI 500 stock pool gaining an excess return of 0.30%, the CSI 800 stock pool gaining 1.60%, and the overall market stock pool gaining 1.59% [7]. - Public fund research selection strategies and private fund research tracking strategies both yielded positive excess returns, with public fund strategies outperforming the CSI 800 by 1.79% and private fund strategies outperforming by 2.77% [8]. - The block trading combination experienced a relative excess return drawdown against the CSI All Index, with an excess return of -0.95% [9]. - The directed issuance combination also faced a relative excess return drawdown against the CSI All Index, with an excess return of -1.50% [10].

本订阅号中所涉及的证券研究信息由光大证券研究所编写，仅面向光大证券专业投资者客户，用作新媒体形势下研究 信息和研究观点的沟通交流。非光大证券专业投资者客户，请勿订阅、接收或使用本订阅号中的任何信息。本订阅号 难以设置访问权限，若给您造成不便，敬请谅解。光大证券研究所不会因关注、收到或阅读本订阅号推送内容而视相 关人员为光大证券的客户。 报告摘要 本周小结：宏观情绪改善，看好铜价继续上行 截至2025年12月12日，SHFE铜收盘价94080 元/吨，环比12月5日+1.40%；LME铜收盘价11553 美元/吨， 环比12月5日-0.96%。（1）宏观：美联储12月如期降息；国内中央经济工作会议强调明年政策"坚持稳中 求进、提质增效"，提出优化"两新"（大规模设备更新和消费品以旧换新）政策，整体利好铜消费。（2） 供需：线缆企业开工率在铜价大涨后本周略有回落，但Q4电网旺季效应仍存；2025Q4空调排产同比下 降，但环比改善；供需仍将维持偏紧格局，继续看好铜价上行。 库存：国内铜社库环比+2.6%，LME铜库存环比+0.8% 点击注册小程序 查看完整报告 特别申明： （1）港口铜精矿库存：截至2025年12月 ...

中信证券：内外兼顾，寻求交集 从此次中央经济工作会议内容来看，做大内循环仍是重心，定位和去年相似。但对于股票市场而言，内需品种 和外需品种的预期和定价与去年存在巨大差异：去年底，投资者对外需普遍谨慎，对内需充满期待，但最终外 需的表现大超预期；今年是重仓布局外需敞口品种，预期相对充分，但对内需品种欠缺信心。实际上，明年外 需继续超预期的难度在加大，但内需可期待的因素在增多。 从这些角度来看，海外敞口品种业绩兑现力强，但估值继续提升难度大；内需敞口品种景气度一般，但一旦超 预期修复，估值弹性不小。配置上要寻求交集，即海外敞口为基底、内需积极变化也会产生催化的品种。 国泰海通：当下是布局春季行情的重要窗口 对于后市，我们比市场共识更乐观：部分投资者以政策表述从"超常规"到"跨周期"解读政策不积极，但这存在 谬误，2025年超常规是相较于2024年尾部风险暴露而言。面向2026年，中央经济工作会议明确"巩固拓展经济 稳中向好势头"，并要求财政政策"更加积极"与"内需主导"，首次提出"推动投资止跌回稳"，并时隔十年重提 房地产"去库存"；中财办副主任韩文秀表示将根据形势变化出台实施增量政策，继续实施"国补"与靠前实 ...

【大河财立方消息】2025年A股已进入收官倒计时，步入年末，市场正处于全年业绩兑现与2026年开局 衔接的关键节点。来年如何布局？跨年行情怎么看？大河财立方记者梳理了6家券商最新解读。 中信建投：跨年行情蓄势待发 中信建投分析师夏凡捷、何盛发表研报认为，从9月初至12月初，AH两地市场经历了较长时间的调整， 投资者情绪趋于谨慎，而近期，多项关键事件与数据相继公布，整体基调符合或略好于市场预期。 中信建投认为，牛市底层逻辑仍在，主要由结构性行情和资本市场改革政策推动。目前市场已经基本完 成调整，叠加基金排名基本落地，跨年有望迎来新一波行情。 中期行业配置方面，中信建投建议重点关注具有一定景气催化的有色金属和AI算力，主题上以商业航 天为主，可控核聚变和人形机器人为辅；港股也具有投资机会，潜在热点板块主要有互联网巨头、创新 药。重点关注：有色、商业航天、AI、人形机器人、可控核聚变、创新药、非银金融等。 中信证券：内外兼顾，寻求交集 中信证券裘翔、刘春彤等人发表研报认为，从此次中央经济工作会议内容来看，做大内循环仍是重心， 定位和去年相似。 研报中提到，但对于股票市场而言，内需品种和外需品种的预期和定价与去年存在 ...