证券代码：300135 证券简称：宝利国际 公告编号：2026-002 江苏宝利国际投资股份有限公司 第七届董事会第三次会议决议公告 本公司及董事会全体成员保证信息披露的内容真实、准确、完整，没有虚假 记载、误导性陈述或重大遗漏。 一、董事会会议召开情况 公司第七届董事会高级管理人员薪酬由基本薪酬、绩效薪酬等构成，依据其 在公司担任的具体职务领取，并按国家政策享受社会保险和公积金等待遇，按公 司统一政策享受补贴及其他福利待遇。在基本薪酬与绩效薪酬总额保持不变的情 况下，绩效薪酬占基本薪酬与绩效薪酬总额的比例调整为不低于 50%。基本薪酬 按月发放，绩效薪酬与公司经营指标完成情况及个人绩效评定挂钩，绩效薪酬按 照预发和递延支付相结合方式发放，递延支付部分在年度报告披露和年度绩效考 核后结算，年度绩效考核依据经审计的财务数据开展，由公司董事会薪酬与考核 委员会考核评定并经董事会审议批准后最终结算，多退少补。高级管理人员在公 司及分子公司兼任其他职务的，不额外领取其他薪酬或报酬。 表决结果：同意 4 票，反对 0 票，弃权 0 票；回避 3 票。议案获得通过。 关联董事笪扬先生、郑毅先生及曾庆波先生回避表决。 本议 ...

格隆汇1月30日丨宝利国际(300135.SZ)在投资者互动平台表示，公司与委内瑞拉暂无直接业务往来。 ...

Core Viewpoint - The company aims to leverage its status as a listed entity and the advantages of being a state-owned enterprise to promote transformation and upgrade, driving high-quality development and enhancing core competitiveness [1] Business Development - The company will fulfill its information disclosure obligations regarding the development of various business segments as required [1]

Core Viewpoint - Baoli International's stock price is influenced by multiple factors including macroeconomic conditions, industry cycles, and the company's operational development [1] Group 1: Company Operations - The company is currently managing its production and operations in an orderly manner [1] - Existing business operations are being deepened and refined [1] Group 2: Strategic Initiatives - The company plans to actively leverage its status as a listed entity and the advantages of state-owned enterprises to promote transformation and upgrading [1] - There is a focus on continuously enhancing core competitiveness and sustainable profitability [1] - The company aims to improve its overall value [1]

针对上述提问，宝利国际回应称："尊敬的投资者，您好，感谢您的关注和建议！公司将积极借助上市 公司平台和国有企业优势促进转型升级，推动公司高质量发展，全面提升公司核心竞争力。关于各项业 务发展情况，公司会按照规定履行信息披露义务，敬请您关注相关公告。谢谢！" 声明：市场有风险，投资需谨慎。本文为AI基于第三方数据生成，仅供参考，不构成个人投资建议。 有投资者在互动平台向宝利国际提问："公司作为国有企业，公司位于池州半导体产业园，半导体产业 园，不断有企业入驻，池州半导体产业园在安徽有重要战略地位，安徽半导体，一核一弧，布局关键节 点，请问公司如何积极对接半导体产业，形成协同效应？再问公司2026年，有没有加大半导体产业的再 投资计划？" 来源：市场资讯 ...

Group 1 - The refining and trading sector increased by 1.46% on January 19, with Bohai Chemical leading the gains [1] - The Shanghai Composite Index closed at 4114.0, up 0.29%, while the Shenzhen Component Index closed at 14294.05, up 0.09% [1] - Key stocks in the refining and trading sector showed significant price increases, with Bohai Chemical rising by 10.02% to a closing price of 4.50 [1] Group 2 - The main capital flow in the refining and trading sector saw a net inflow of 562 million yuan, while retail investors experienced a net outflow of 475 million yuan [2] - Notable stock performances included Hengli Petrochemical, which rose by 6.91% to a closing price of 25.08, and Runbei Hangke, which increased by 6.04% to 42.13 [1][2] - The trading volume for Bohai Chemical reached 956,500 shares, contributing to a transaction value of 413 million yuan [1]

Core Viewpoint - The company is actively seeking opportunities for transformation and upgrading while managing its existing business operations, indicating a focus on capturing new investment and business growth opportunities [1] Group 1 - Investors have inquired about the company's plans to consider mergers and acquisitions in related industries to improve performance and reshape market expectations [1] - The company acknowledges the pressure on its operations in recent years and expresses a commitment to exploring new growth avenues [1] - The management emphasizes compliance with information disclosure obligations regarding any significant related matters [1]

证券代码：300135 证券简称：宝利国际 公告编号：2026-001 江苏宝利国际投资股份有限公司 关于累计诉讼、仲裁事项的进展公告 四、其他前期已披露累计诉讼、仲裁事项进展 就宝利建设诉吉林城建吉林市新城大桥工程项目合同纠纷一案，因吉林城建 未履行法院二审判决，公司向法院提出了强制执行申请。近日，公司收到吉林省 吉林市中级人民法院通知，吉林省吉林市中级人民法院已于 2026 年 1 月 4 日对 公司强制执行申请予以立案执行，案号为(2026)吉 02 执 40 号。 二、是否有其他尚未披露的重大仲裁及诉讼 截至本公告披露日，除已披露的诉讼、仲裁事项外，公司及控股子公司存在 的其他小额诉讼、仲裁事项未达到《深圳证券交易所创业板股票上市规则》等规 定的披露标准，公司及控股子公司无应披露而未披露的其他诉讼、仲裁事项。 三、本次公告的诉讼、仲裁对公司本期利润或期后利润的可能影响 就本次公告进展的诉讼事项，公司已对相关应收账款全额计提减值，并已在 公司过往定期报告中予以反映，若公司在后续强制执行过程中收回全部或部分判 决款项，将对公司利润产生正面影响，最终会计处理及对公司利润的影响数据将 以审计机构年度审计确认后 ...

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]

Group 1 - The refining and trading sector experienced a slight decline of 0.03% on December 25, with Daqing Huake leading the drop [1] - The Shanghai Composite Index closed at 3959.62, up 0.47%, while the Shenzhen Component Index closed at 13531.41, up 0.33% [1] - Notable gainers in the refining and trading sector included Baomo Co., which rose by 4.25% to a closing price of 6.38, and Hengyi Petrochemical, which increased by 3.96% to 9.71 [1] Group 2 - Daqing Huake saw a significant decline of 6.37%, closing at 20.30, with a trading volume of 155,500 shares and a transaction value of 319 million [2] - The overall net capital outflow from the refining and trading sector was 440 million, while retail investors saw a net inflow of 253 million [2] - The capital flow analysis indicated that Hengli Petrochemical had a net outflow of 29.23 million from institutional investors, while Baomo Co. had a net inflow of 6.43 million [3]