便捷式发电机 0 1 定义及研究范围 便携式发电机是一种小型发电设备，通常由汽油、柴油或天然气驱动，用于在停电或无主电 源的情况下提供临时电力支持。其广泛应用于家庭、户外露营、施工工地、应急救援和小型 商业场所。随着自然灾害频发、电力供应不稳定以及人们对户外生活需求的增长，便携式发 电机市场持续扩大，成为备电领域的重要解决方案。现代便携式发电机具备噪音低、操作简 便、燃油效率高等特点，部分型号还支持多燃料兼容、智能监控等功能，满足不同用户的多 样化需求。 2025 年全球压缩机产量达 682.5 万，平均售价为 423 元 / 台。 从上游来看，便携式发电机主要原材料包括金属、塑料等原材料及发动机、发电机头等配 件，配件供应商包括 Honda 、 Yamaha 、 Briggs & Stratton 以及隆鑫等企业，金属主要 来自宝武集团、河钢集团、沙钢集团等大型钢铁供应体系；塑料与高分子材料代表性供应商 包括浙江三和塑料有限公司、浙江金立达塑材有限公司、巴斯夫等。 根据 QYResearch 头部企业研究中心调研，全球范围内便携式发电机生产商主要包括宗 申、重庆润通、隆鑫通用、神驰机电、本田（ Honda ...

0226强势股脱水 | 哪些券商还有整合预期？ 2025/02/26 16:25 ①券商：尾盘市场突现大作文，近期几大AMC将中金、银河股份转让至中央汇金，在自上而下的 政策倡导及自下而上的公司自身推动下，预计并购重组浪潮仍是券商行业的主线。 ②机器人丝杠：公司原来的汽车产品就是国内独家，现在成功研发部分人形机器人与车用丝杠产 品，已实现小批量产品销售，近期还和另一家上市公司合作未来努力抢占客户。 ③钢铁：产业链需求近期回升，行业处于主动补库存状态，带动价格回升，同时作为低估值代表 板块，具备规模效应的龙头钢企，未来存在估值修复的机会。 本文是对当日大涨公司进行研报深度复盘，相关个股信息仅供参考，不构成投资建议。 1、券商：整合预期 （1）大涨题材：券商 券商尾盘大幅异动，其中近期完成实控人变更的中金公司和中国银河表现最为突出。 2月14日，几大AMC将其所持有多家上市公司股份转让至中央汇金，在未完成划转前，中央汇 金公司旗下的证券公司包括中金公司、申万宏源、中国银河、中信建投。划转完成后，东兴 证券、信达证券等实控人也变为中央汇金公司。 机构认为，并购重组加速行业集中度提升，关注股东背景相近的央企券商及同地 ...

钢铁板块估值延续修复 【】【】 钢铁 [Table_Industry] [Table_ReportDate] 2026 年 3 月 1 日 证券研究报告 行业研究——周报 [Tabl 行业周报 e_ReportType] [高Table_Author] 升 煤炭、钢铁行业首席分析师 执业编号：S1500524100002 邮 箱：gaosheng@cindasc.com 刘 波 煤炭、钢铁行业分析师 执业编号：S1500525070001 邮 箱：liubo1@cindasc.com 李 睿 煤炭、钢铁行业分析师 执业编号：S1500525040002 邮箱：lirui@cindasc.com 信达证券股份有限公司 CINDA SECURITIES CO.，LTD 北京市西城区宣武门西大街甲127号金隅 大厦B座 邮编：100031 [钢铁板块估值延续修复 Table_Title] 本期内容提要： ➢ 本周市场表现：本周钢铁板块上涨 11.80%，表现优于大盘；其中， 特钢板块上涨 12.77%，长材板块上涨 9.30%，板材板块上涨 11.5 3%；铁矿石板块上涨 12.32%，钢铁耗材板块上涨 14.57 ...

交易咨询业务资格：证监许可【2012】1091号 2026-02-27锰硅早报 大越期货投资咨询部 胡毓秀 从业资格证号：F03105325 投资咨询证号：Z0021337 联系方式：0575-85226759 重要提示：本报告非期货交易咨询业务项下服务，其中的观点和信息仅作参考之用，不构成对任何人的投 资建议。 我司不会因为关注、收到或阅读本报告内容而视相关人员为客户；市场有风险，投资需谨慎。 每日观点 锰硅2605： 1.基本面：2026年春节假期期间，硅锰价格维持平稳，合金厂开工波动不大，展望节后市场，硅锰预计仍将面临成本高位 与供应宽松的双重博弈。成本端，锰矿价格维持偏强运行，内蒙古及南方地区电价存在上涨预期，焦炭价格维持高位，预 计节后硅锰综合成本将易涨难跌，为现货价格提供了较强的底部支撑。供应端，内蒙古新增产能仍有个别工厂存在点火预 期，另外，宁夏厂家库存高位，若终端需求复苏不及预期，硅锰供应端压力将进一步凸显。总体来看，硅锰市场将维持震 荡运行态势。受成本支撑影响，硅锰价格下行空间有限，但供应端压制导致价格上冲的动力不足，需重点观望新增产能点 火，主产区电价波动情况以及主流钢厂招标定价情况；中性 ...

Quantitative Models and Construction Methods 1. Model Name: GAN_GRU Model - **Model Construction Idea**: The GAN_GRU model combines Generative Adversarial Networks (GAN) for feature generation and Gated Recurrent Unit (GRU) for time-series feature encoding to create a stock selection factor[4][13][14] - **Model Construction Process**: - **GAN Component**: - The GAN consists of a generator (G) and a discriminator (D). The generator learns the real data distribution and generates realistic samples, while the discriminator distinguishes between real and generated data[23][24] - Generator loss function: $$L_{G} = -\mathbb{E}_{z\sim P_{z}(z)}[\log(D(G(z)))]$$ where \(z\) is random noise, \(G(z)\) is the generated data, and \(D(G(z))\) is the discriminator's output probability for generated data being real[24][25] - Discriminator loss function: $$L_{D} = -\mathbb{E}_{x\sim P_{data}(x)}[\log D(x)] - \mathbb{E}_{z\sim P_{z}(z)}[\log(1-D(G(z)))]$$ where \(x\) is real data, \(D(x)\) is the discriminator's output probability for real data, and \(D(G(z))\) is the discriminator's output probability for generated data[27][29] - GAN training alternates between updating the generator and discriminator parameters through backpropagation[30] - The generator uses an LSTM model to preserve the sequential nature of input features, while the discriminator employs a CNN model to process the 2D structure of the generated features[33][37] - **GRU Component**: - Two GRU layers (GRU(128, 128)) are used, followed by an MLP (256, 64, 64) to output predicted returns (\(pRet\)) as the stock selection factor[22] - Input features include 18 price-volume characteristics (e.g., closing price, turnover rate) sampled over the past 40 days to predict cumulative returns for the next 20 trading days[14][18] - Data preprocessing includes outlier removal, standardization, and cross-sectional normalization[18] - Training is conducted semi-annually with rolling updates, using Adam optimizer, a learning rate of \(1e-4\), and IC as the loss function[18] - **Model Evaluation**: The GAN_GRU model effectively integrates GAN's feature generation capabilities with GRU's time-series encoding, making it suitable for capturing complex price-volume patterns in stock selection[4][13] --- Model Backtesting Results GAN_GRU Model - **IC Mean**: 0.1096*** (2019.02–2026.02)[41] - **ICIR (Non-Annualized)**: 0.87[42] - **Turnover Rate**: 0.82X[42] - **Recent IC**: -0.0105*** (latest period)[41][42] - **1-Year IC Mean**: 0.0517***[41][42] - **Annualized Return**: 38.13%[42] - **Annualized Volatility**: 23.18%[42] - **IR**: 1.64[42] - **Maximum Drawdown**: 27.29%[42] - **Annualized Excess Return**: 22.32%[41][42]

- The GAN_GRU factor is based on the GAN_GRU model, which utilizes Generative Adversarial Networks (GAN) for processing volume-price time series features and then employs the GRU model for time series feature encoding to derive the stock selection factor[4][13] - The GAN_GRU model includes two GRU layers (GRU(128, 128)) followed by an MLP (256, 64, 64), with the final output being the predicted return (pRet) used as the stock selection factor[22] - The GAN model consists of a generator and a discriminator. The generator aims to generate data that appears real, while the discriminator aims to distinguish between real and generated data. The generator's loss function is $L_{G} = -\mathbb{E}_{z\sim P_{z}(z)}[\log(D(G(z)))]$ and the discriminator's loss function is $L_{D} = -\mathbb{E}_{x\sim P_{d a t a}(x)}[\log D(x)] - \mathbb{E}_{z\sim P_{z}(z)}[\log(1-D(G(z)))]$[23][24][27] - The GAN_GRU model's training process involves alternating between training the generator and the discriminator until convergence[29][30] - The GAN_GRU factor's performance from February 2019 to February 2026 shows an IC mean of 0.1096, an annualized excess return of 22.32%, and an ICIR (non-annualized) of 0.87[41][42] - The latest IC value as of February 25, 2026, is -0.0105, with a one-year IC mean of 0.0517[41][42] - The top five industries for the GAN_GRU factor in February 2026, based on IC, are Electric Utilities, Retail, Real Estate, Construction, and Basic Chemicals, with IC values of 0.1257, 0.1196, 0.1151, 0.1130, and 0.1063, respectively[44] - The top five industries for the GAN_GRU factor over the past year, based on IC mean, are Steel, Computers, Media, Retail, and Food & Beverage, with IC means of 0.1404, 0.1175, 0.1132, 0.1014, and 0.0989, respectively[44] - The top five industries for the GAN_GRU factor's long positions in February 2026, based on excess returns, are Oil & Petrochemicals, Communications, Electronics, Non-ferrous Metals, and Computers, with excess returns of 7.91%, 3.11%, 3.06%, 2.78%, and 2.78%, respectively[45] - The top five industries for the GAN_GRU factor's long positions over the past year, based on average monthly excess returns, are Real Estate, Retail, Automobiles, Construction, and Consumer Services, with excess returns of 3.83%, 2.04%, 1.93%, 1.50%, and 1.49%, respectively[46] **Performance Metrics of GAN_GRU Factor:** - IC: 0.1096[41][42] - ICIR (non-annualized): 0.87[41][42] - Turnover Rate: 0.82X[41][42] - Recent IC: -0.0105[41][42] - One-year IC: 0.0517[41][42] - Annualized Return: 38.13%[41][42] - Annualized Volatility: 23.18%[41][42] - Information Ratio (IR): 1.64[41][42] - Maximum Drawdown: 27.29%[41][42] - Annualized Excess Return: 22.32%[41][42]

Group 1 - The core viewpoint of the news is that Jiangsu Shagang Co., Ltd. has seen a stock price increase of 5.05%, reaching 5.83 yuan per share, with a trading volume of 223 million yuan and a turnover rate of 1.78%, resulting in a total market capitalization of 12.79 billion yuan [1] - Jiangsu Shagang Co., Ltd. was established on September 28, 1999, and listed on October 25, 2006, primarily engaged in the production and sales of special steel through black metal smelting and rolling processing [1] - The main business revenue composition of Jiangsu Shagang is 93.25% from steel and steel billet sales, while other sales account for 6.75% [1] Group 2 - From the perspective of the top ten circulating shareholders of Jiangsu Shagang, a fund under Southern Fund ranks among them, specifically the Southern CSI 1000 ETF (512100), which reduced its holdings by 155,100 shares in the third quarter, now holding 16.0571 million shares, representing 0.73% of the circulating shares [2] - The Southern CSI 1000 ETF (512100) was established on September 29, 2016, with a latest scale of 78.996 billion yuan, achieving a year-to-date return of 9.26% and a one-year return of 30.14% [2] - The fund manager of Southern CSI 1000 ETF (512100) is Cui Lei, who has a cumulative tenure of 7 years and 112 days, with the fund's total asset scale at 137.02 billion yuan, achieving the best return of 250.66% and the worst return of -15.93% during the tenure [2]

内容概况：我国在钢渣综合利用方面的研究起步相对较晚。近年来，随着国家对环境保护和固体废弃物 资源化利用的重视程度不断提升，钢渣在筑路、建材以及混凝土等领域的应用逐步拓展。钢渣的有效综 合利用不仅能够回收其中的废钢和金属铁，还能显著提高钢铁企业矿产资源的整体利用率，有助于降低 生产成本，并缓解因钢渣堆存带来的土地占用和环境污染等生态问题。这对推动钢铁企业循环经济发 展、实现节能减排目标以及促进行业可持续发展具有重要意义。数据显示，2025年中国钢渣利用规模约 为12891万吨，同比增长2.8%。未来，随着技术持续进步、政策支持强化以及资源化标准体系的完善， 中国钢渣利用规模有望进一步扩大。 相关上市企业：方大特钢（600507）、首钢股份（000959）、沙钢股份（002075）、华菱钢铁 （000932）、山东钢铁（600022）、杭钢股份（600126）、柳钢股份（601003）、中国建筑 （601668）、中国中铁（601390）、中国铁建（601186）等。 相关企业：郑州沃特节能科技股份有限公司、江苏融达新材料股份有限公司、上海中冶环境工程科技有 限公司、河北物华循环资源有限公司、宝武集团环境资源科技 ...

内容概要：电工钢作为电力、电子和军事工业的关键软磁合金材料，以高磁导率与低铁损特性，成为电 机、变压器等设备的核心材料。近年来，国内产业结构优化推动电工钢行业规模化、高端化同步发展， 2020-2025年产量与消费量连续扩容，2025年供需规模均创历史新高。产能扩张呈现品类与区域双重集 中，新增产能聚焦高端品类，华东、华中、华北成为主要集聚区，预计2026-2027年产能集中释放，市 场竞争或加剧。细分市场中，无取向电工钢占据主流，支撑新能源汽车、家电等需求；取向电工钢增速 显著，支撑特高压、新能源发电等高端需求。进出口方面，中国形成"出口引领、进口补充"格局，净出 口规模扩大，出口均价高于进口，反映技术升级与产品优化。下游需求以电机与变压器为核心，新能源 汽车、风电光伏等新兴产业成为增长极。竞争格局上，宝钢股份与首钢智新双龙头引领，细分领域梯队 分化。未来，行业将向高端化、集中化、绿色智能化发展，适配高端装备需求，提升发展质量与效益。 上市企业：宝钢股份（600019.SH）、沙钢股份（002075.SZ）、鞍钢股份（000898.SZ） 相关企业：首钢智新电磁材料（迁安）股份有限公司、湖南宏旺新材料科技有 ...

Core Viewpoint - The steel sector is expected to have strong "anti-involution" characteristics and significant profit recovery potential, making it a strategic investment opportunity in the medium to long term, with a "positive" industry rating maintained [1] Market Performance - Last week, the steel sector rose by 1.01%, outperforming the broader market; the special steel sector increased by 1.80%, while long products fell by 3.15% and flat products rose by 1.24% [2] - Iron ore sector increased by 4.12%, while steel consumption materials and trade circulation sectors fell by 1.61% and 1.78% respectively [2] Supply Situation - As of February 13, the capacity utilization rate of blast furnaces among sample steel companies was 86.4%, up by 0.72 percentage points week-on-week [2] - Electric furnace capacity utilization was 21.0%, down by 27.11 percentage points week-on-week [2] - The production of five major steel products was 6.96 million tons, a decrease of 248,600 tons or 3.45% week-on-week [2] Demand Situation - As of February 13, the consumption of five major steel products was 6.891 million tons, down by 715,800 tons or 9.41% week-on-week [2] - The transaction volume of construction steel among mainstream traders was 35,000 tons, down by 48.24% week-on-week [2] Inventory Situation - As of February 13, social inventory of five major steel products was 10.267 million tons, up by 9.17% week-on-week [3] - Factory inventory was 4.161 million tons, also up by 4.71% week-on-week [3] Price and Profit Situation - As of February 13, the comprehensive index for ordinary steel was 3,409.5 yuan/ton, down by 0.14% week-on-week [3] - The comprehensive index for special steel was 6,579.7 yuan/ton, down by 0.03% week-on-week [3] - The profit for rebar was 80 yuan/ton, up by 23.08% week-on-week [3] Raw Material Situation - As of February 14, the spot price index for Australian powder ore (62% Fe) was 754 yuan/ton, down by 1.44% week-on-week [4] - The price for primary metallurgical coke was 1,770 yuan/ton, unchanged week-on-week [4] Overall Assessment - The current inventory pressure for the five major steel products is relatively limited, with overall inventory at a historically low level and accumulation speed slower than previous years [5] - The profit margins for ordinary steel are favorable, indicating significant improvement potential for ordinary steel companies, which may lead to value recovery in the steel sector [5]