| 证券代码：000709 | 股票简称：河钢股份 公告编号：2026-001 | | --- | --- | | 债券简称：24HBIS01 | 债券代码：148581.SZ | 河钢股份有限公司 2024 年面向专业投资者公开发行公司债券（第一期） 2026 年付息公告 本公司及全体董事、高级管理人员保证公告内容的真实、准确和完整，没 有虚假记载、误导性陈述或者重大遗漏。 重要提示： 1 债券简称：24HBIS01 债券代码：148581 债权登记日：2026 年 1 月 16 日 付息日：2026 年 1 月 19 日 计息期间：2025 年 1 月 18 日至 2026 年 1 月 17 日 河钢股份有限公司 2024 年面向专业投资者公开发行公司债券(第一期)（以 下简称"本期债券"）将于 2026 年 1 月 19 日支付自 2025 年 1 月 18 日至 2026 年 1 月 17 日期间的利息，为确保付息工作的顺利进行，现将有关事项公告如下： 一、本期债券基本情况 1、发行人：河钢股份有限公司 2、债券名称：河钢股份有限公司 2024 年面向专业投资者公开发行公司债券 (第一期) 3、债券简 ...

| 证券代码：000709 | 股票简称：河钢股份 | 公告编号：2026-001 | | --- | --- | --- | | 债券简称：24HBIS01 | 债券代码：148581.SZ | | 河钢股份有限公司 2024 年面向专业投资者公开发行公司债券（第一期） 2026 年付息公告 本公司及全体董事、高级管理人员保证公告内容的真实、准确和完整，没 有虚假记载、误导性陈述或者重大遗漏。 重要提示： 5、债券余额：15.00 亿元 6、期限：本期债券为 3.00 年固定利率债券。 7、当前票面利率：本期债券在本计息期内票面利率为 2.98% 1 债券简称：24HBIS01 债券代码：148581 债权登记日：2026 年 1 月 16 日 付息日：2026 年 1 月 19 日 计息期间：2025 年 1 月 18 日至 2026 年 1 月 17 日 河钢股份有限公司 2024 年面向专业投资者公开发行公司债券(第一期)（以 下简称"本期债券"）将于 2026 年 1 月 19 日支付自 2025 年 1 月 18 日至 2026 年 1 月 17 日期间的利息，为确保付息工作的顺利进行，现将有关事 ...

Market Overview - On January 14, the general steel sector experienced a decline of 0.2% compared to the previous trading day, with Lingang Co., Ltd. leading the drop [1] - The Shanghai Composite Index closed at 4126.09, down 0.31%, while the Shenzhen Component Index closed at 14248.6, up 0.56% [1] Individual Stock Performance - Hangang Co., Ltd. saw a significant increase in its stock price, closing at 65.6 with a rise of 8.12%, and a trading volume of 2.91 million shares, amounting to 2.772 billion yuan [1] - Other notable performers included Jiugang Hongxing, which rose by 3.74% to close at 1.94, and Xinxing Ductile Iron Pipes, which increased by 2.83% to close at 4.36 [1] - Conversely, Lingang Co., Ltd. closed at 2.11, down 2.76%, with a trading volume of 402,500 shares and a transaction value of 86.302 million yuan [2] Capital Flow Analysis - The general steel sector saw a net inflow of 278 million yuan from main funds, while retail investors experienced a net outflow of 227 million yuan [2] - The main funds showed significant net inflows in stocks like Hangang Co., Ltd. (495.1 million yuan) and Xinxing Ductile Iron Pipes (49.14 million yuan) [3] - Retail investors had notable outflows in stocks such as Hangang Co., Ltd. (-263 million yuan) and Xinxing Ductile Iron Pipes (-45.12 million yuan) [3]

近日，河钢获签的2026年度河北省 首张钢材出口许可证订单顺利完成报关 河北省签发的首张证书 根据商务部、海关总署公告 自2026年1月1日起 对钢材、生铁及钢坯等部分钢铁产品 实行出口许可证管理 并要求企业提供产品质量检验合格证明 旨在引导行业优化出口结构 促进钢铁产业高质量发展 本次涉及的23份出口许可证业务中 既包含河钢传统优势产品 也涵盖高性能管线钢、定制汽车钢、 绿色低碳钢材等高附加值品种 对企业合规运营、产品升级 与市场适配性提出了新要求 河钢深入贯彻河北省 即将发运至孟加拉国吉大港 这是我国时隔16年重启钢材出口许可证管理后 钢材出口许可证管理政策优化调整工作部署 持续深化政企协同 坚持"高端化、智能化、绿色化、 融合化、国际化"发展 持续优化出口产品结构 进一步拓宽国际钢铁贸易布局 通过构建"政策解读—合规审核— 深化与全球客户的合作纽带 稳步增强国际市场钢材贸易综合竞争力 面对钢材出口许可证管理政策的优化调整 河钢迅速响应、高效推进 组建专项工作组 协同河钢产销研体系及各业务单元 流程优化—客户协同"的全周期服务机制 不断提升跨境业务运作效率 系统梳理产品目录、全面解读政策、优化申报流程 超 ...

Core Viewpoint - The company emphasizes its commitment to risk management and aims to ensure stable business development [2] Group 1 - The company is actively working on various risk prevention measures [2] - The company is focused on promoting steady growth in its operations [2]

证券日报网讯1月12日，河钢股份（000709）在互动平台回答投资者提问时表示，公司截至2025年12月 31日的股东人数为231601户。 ...

Group 1 - Abu Dhabi's BlueFive Capital has been appointed as the first general partner of a 32 billion RMB fund established by CICC Capital and Hebei Iron and Steel Group [1] - The fund focuses on advanced materials, new energy, and next-generation information technology sectors to promote cross-border investment and industrial upgrading [1] - BlueFive Capital aims to support the international development of China's high-end manufacturing [1]

Quantitative Models and Construction Methods 1. Model Name: GAN_GRU - **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 construct a stock selection factor[4][13][14] - **Model Construction Process**: 1. **GAN Component**: - The generator (G) learns the real data distribution and generates realistic samples from random noise \( z \) (Gaussian or uniform distribution). The generator's loss function is: $$ L_{G} = -\mathbb{E}_{z\sim P_{z}(z)}[\log(D(G(z)))] $$ where \( D(G(z)) \) represents the discriminator's probability of classifying generated data as real[24][25][26] - The discriminator (D) distinguishes real data from generated data. Its loss function is: $$ L_{D} = -\mathbb{E}_{x\sim P_{data}(x)}[\log D(x)] - \mathbb{E}_{z\sim P_{z}(z)}[\log(1-D(G(z)))] $$ where \( D(x) \) is the probability of real data being classified as real, and \( D(G(z)) \) is the probability of generated data being classified as real[27][29][30] - GAN training alternates between optimizing \( G \) and \( D \) until convergence[30] 2. **GRU Component**: - Two GRU layers (GRU(128, 128)) are used to encode time-series features, followed by a Multi-Layer Perceptron (MLP) with layers (256, 64, 64) to predict returns. The final output \( pRet \) is used as the stock selection factor[22] 3. **Feature Input and Processing**: - Input features include 18 price-volume characteristics (e.g., closing price, turnover, etc.) sampled over the past 400 days, with a shape of \( 40 \times 18 \) (40 days of features)[18][19][37] - Features undergo outlier removal, standardization, and cross-sectional normalization[18] 4. **Training Details**: - Training-validation split: 80%-20% - Semi-annual rolling training (June 30 and December 31 each year) - Hyperparameters: batch size equals the number of stocks, Adam optimizer, learning rate \( 1e-4 \), IC loss function, early stopping (10 rounds), max training rounds (50)[18] 5. **Stock Selection**: - Stocks are filtered to exclude ST stocks and those listed for less than six months[18] - **Model Evaluation**: The GAN_GRU model effectively captures price-volume time-series features and demonstrates strong predictive power for stock returns[4][13][22] --- Model Backtesting Results 1. GAN_GRU Model - **IC Mean**: 0.1119*** (2019-2025)[4][41] - **ICIR (non-annualized)**: 0.89[42] - **Turnover Rate**: 0.83X[42] - **Recent IC**: 0.0331*** (December 2025)[4][41] - **1-Year IC Mean**: 0.0669***[4][41] - **Annualized Return**: 37.40%[42] - **Annualized Volatility**: 23.39%[42] - **IR**: 1.60[42] - **Maximum Drawdown**: 27.29%[42] - **Annualized Excess Return**: 22.42%[4][42] --- Quantitative Factors and Construction Methods 1. Factor Name: GAN_GRU Factor - **Factor Construction Idea**: The GAN_GRU factor is derived from the GAN_GRU model, leveraging GAN for price-volume feature generation and GRU for time-series encoding[4][13][14] - **Factor Construction Process**: - The GAN generator processes raw price-volume time-series features (\( Input\_Shape = 40 \times 18 \)) and outputs transformed features with the same shape (\( Input\_Shape = 40 \times 18 \))[37] - The GRU component encodes these features into a predictive factor for stock selection[22] - The factor undergoes industry and market capitalization neutralization and standardization[22] - **Factor Evaluation**: The GAN_GRU factor demonstrates robust performance across various industries and time periods, with significant IC values and excess returns[4][41] --- Factor Backtesting Results 1. GAN_GRU Factor - **IC Mean**: 0.1119*** (2019-2025)[4][41] - **ICIR (non-annualized)**: 0.89[42] - **Turnover Rate**: 0.83X[42] - **Recent IC**: 0.0331*** (December 2025)[4][41] - **1-Year IC Mean**: 0.0669***[4][41] - **Annualized Return**: 37.40%[42] - **Annualized Volatility**: 23.39%[42] - **IR**: 1.60[42] - **Maximum Drawdown**: 27.29%[42] - **Annualized Excess Return**: 22.42%[4][42] 2. Industry-Specific Performance - **Top 5 Industries by Recent IC (October 2025)**: - Social Services: 0.4243*** - Coal: 0.2643*** - Environmental Protection: 0.2262*** - Retail: 0.1888*** - Steel: 0.1812***[4][41][42] - **Top 5 Industries by 1-Year IC Mean**: - Social Services: 0.1303*** - Steel: 0.1154*** - Non-Bank Financials: 0.1157*** - Retail: 0.1067*** - Building Materials: 0.1017***[4][41][42] 3. Industry-Specific Excess Returns - **Top 5 Industries by December 2025 Excess Returns**: - Banking: 4.30% - Real Estate: 3.51% - Environmental Protection: 2.18% - Retail: 1.76% - Machinery: 1.71%[2][45] - **Top 5 Industries by 1-Year Average Excess Returns**: - Banking: 2.12% - Real Estate: 1.93% - Environmental Protection: 1.50% - Retail: 1.46% - Machinery: 1.23%[2][46]

Investment Rating - The report maintains a "Recommend" rating for the steel industry [1] Core Insights - The overall profitability of the steel industry is recovering rapidly, with effective cost control measures leading to significant improvements in profit margins [5][6] - In Q3 2025, the steel sector achieved record profits, with net profit margins increasing quarterly, indicating a sustainable recovery in profitability [7][10] - The recovery in profitability is broadening, with leading companies showing faster recovery rates compared to their peers, highlighting the advantages of scale and cost management [19][24] - Investment recommendations focus on companies with stable high dividends, those with high technical barriers, and upstream resource companies benefiting from improved supply dynamics [27] Summary by Sections 1. Industry Profitability Recovery and Cost Control - The profitability of the steel industry has shown significant improvement, with a total profit of 111.5 billion yuan from January to November 2025, a year-on-year increase of 1,752.2% [5] - The SW steel sector reported a net profit of 20.147 billion yuan in the first three quarters of 2025, marking a turnaround from losses in the previous year [5][6] 2. Record Profits in Q3 2025 - In Q3 2025, the steel sector generated revenue of 480.123 billion yuan, a slight year-on-year increase of 0.07%, while costs decreased by 4.42% [7][8] - The net profit for Q3 2025 reached 8.716 billion yuan, reflecting a 12.16% increase from the previous quarter and a significant recovery from losses in the same period last year [7][10] 3. Broadening Profit Recovery and Leading Companies - Leading steel companies like Baosteel and Shougang have demonstrated strong profit recovery, with Shougang's net profit increasing by 368.13% year-on-year [19][20] - The recovery in profitability is not limited to top firms; several smaller companies have also turned losses into profits, indicating a broad improvement across the sector [24] 4. Investment Recommendations - The report suggests focusing on companies with stable high dividends, such as CITIC Special Steel and Hualing Steel, as well as those with high technical barriers like Fangda Special Steel [27] - Upstream resource companies like Baotou Steel and Hainan Mining are also recommended due to expected improvements in supply dynamics [27]

Group 1 - The core point of the announcement is the resolution of the third extraordinary general meeting of shareholders held by Hebei Iron and Steel Co., Ltd., which confirmed the approval of several proposals without any dissenting votes [1][2] - The meeting was held on December 29, 2025, with a combination of on-site and online voting, ensuring compliance with relevant laws and regulations [3][4] Group 2 - A total of 497 shareholders attended the meeting, representing 6,871,950,746 shares, which accounts for 66.4784% of the total voting shares [5] - Among the attending shareholders, 493 were small shareholders, representing 176,713,698 shares, or 1.7095% of the total voting shares [6] Group 3 - Proposal 1 regarding the expected daily related transactions for 2026 was approved with 142,089,324 votes in favor, representing 80.4065% of the valid votes [9] - Proposal 2 concerning the capital increase to the subsidiary Hebei Letin Steel Co., Ltd. was also approved with 140,552,820 votes in favor, accounting for 79.5370% of the valid votes [11] - Proposal 3, which involved amendments to the company's articles of association, received overwhelming support with 6,851,688,788 votes in favor, representing 99.7051% of the valid votes [13] Group 4 - The legal opinion provided by the law firm confirmed that the meeting's procedures and resolutions were in accordance with relevant laws and the company's articles of association, validating the legitimacy of the meeting [15]