截至2026年2月5日收盘，宁波高发(603788)报收于16.37元，下跌0.3%，换手率0.78%，成交量1.75万 手，成交额2862.6万元。 宁波高发汽车控制系统股份有限公司使用闲置自有资金进行委托理财，累计滚动购买理财产品22,000万 元，包括国债逆回购、结构性存款及证券公司收益凭证等，受托方涵盖国信证券、浦发银行、招商银 行、交通银行、宁波银行等金融机构。投资期限从短期至一年内不等，资金来源为公司闲置自有资金， 部分为前期理财到期资金。产品类型多为中低风险、保本浮动收益型，存在市场、流动性、信用等风 险，收益具有不确定性。公司已履行相关审议程序，董事会及相关部门将监督资金使用情况。 以上内容为证券之星据公开信息整理，由AI算法生成（网信算备310104345710301240019号），不构成 投资建议。 来自交易信息汇总：2月5日主力资金净流入75.92万元，占总成交额2.65%。 来自公司公告汇总：宁波高发使用闲置自有资金累计滚动购买理财产品22,000万元，涵盖国债逆 回购、结构性存款及证券公司收益凭证等。 交易信息汇总资金流向 2月5日主力资金净流入75.92万元，占总成交额2.65% ...

登录新浪财经APP 搜索【信披】查看更多考评等级 本公司董事会及全体董事保证本公告内容不存在任何虚假记载、误导性陈述或者重大遗漏，并对其内容 的真实性、准确性和完整性承担法律责任。 重要内容提示： 为提高资金使用效率，合理利用资金，创造更大的经济效益，公司对最高额度不超过人民币50,000万元 的闲置自有资金用于进行结构性存款及购买银行、证券公司或信托公司等金融机构理财产品。在保证公 司正常经营所需流动资金的情况下滚动使用。 履行的审议程序：公司第五届董事会第十二次会议审议通过了《关于使用闲置自有资金进行结构性存款 或购买理财产品的议案》，该议案业经公司2024年年度股东大会审议批准，详情请参见公司公告（公告 编号：2025-011、2025-019）。 本次资金来源为公司暂时闲置的自有资金，部分为前期理财到期后资金。公司于2025年11月7日在上海 证券交易所网站披露了《关于使用闲置自有资金委托理财的进展公告》（公告编号：2025-040） 二、本次委托理财进展/风险情况 2025年11月7日至本公告披露日，公司使用闲置自有资金人民币22,000万元滚动购买了国债逆回购、国 信证券股份有限公司、上海浦东发展 ...

2月4日主力资金净流出232.63万元，占总成交额6.62%；游资资金净流入156.77万元，占总成交额 4.46%；散户资金净流入75.86万元，占总成交额2.16%。 公司公告汇总关于使用闲置自有资金委托理财的进展公告 宁波高发汽车控制系统股份有限公司使用闲置自有资金进行委托理财，累计滚动购买理财产品22,000万 元，包括国债逆回购、结构性存款及证券公司收益凭证等，受托方涵盖国信证券、浦发银行、招商银 行、交通银行、宁波银行等金融机构。投资期限从短期至一年内不等，资金来源为公司闲置自有资金， 部分为前期理财到期资金。产品类型多为中低风险、保本浮动收益型，存在市场、流动性、信用等风 险，收益具有不确定性。公司已履行相关审议程序，董事会及相关部门将监督资金使用情况。 截至2026年2月4日收盘，宁波高发(603788)报收于16.42元，上涨1.3%，换手率0.96%，成交量2.15万 手，成交额3515.83万元。 当日关注点 交易信息汇总资金流向 来自交易信息汇总：2月4日主力资金净流出232.63万元，占总成交额6.62%。 来自公司公告汇总：宁波高发使用闲置自有资金累计滚动购买理财产品22,000万 ...

证券代码：603788 证券简称：宁波高发 公告编号：2026-001 宁波高发汽车控制系统股份有限公司 关于使用闲置自有资金委托理财的进展公告 | 产品名称 | 招商银行点金系列看涨两层区间 90 天结构性存款（产品代码： | | --- | --- | | | NNB01895） | | 受托方名称 | 招商银行股份有限公司 | | 购买金额 | 万元 4,000 | | 产品期限 | 2025/12/30-2026/03/30 | 重要内容提示： 基本情况 | 产品名称 | 国债逆回购（204007-GC007） | | --- | --- | | 受托方名称 | 国信证券股份有限公司 | | 购买金额 | 3,000 万元 | | 产品期限 | 2025/12/26-2026/01/05 | | 产品名称 | 国信证券股份有限公司收益凭证【稳健增益 期】 9 | | --- | --- | | 受托方名称 | 国信证券股份有限公司 | | 购买金额 | 万元 1,000 | | 产品期限 | 2026/1/6-2026/11/26 | | 产品名称 | 国信证券股份有限公司收益凭证【鑫安看涨 23 期 ...

来源：市场资讯 天眼查工商信息显示，近日，宁波高发汽车控制系统股份有限公司出资100万元成立高发国际投资（海 南）有限公司，持股100%，所属行业为商务服务业。 资料显示，高发国际投资（海南）有限公司成立于2025年8月8日，法定代表人为钱高法，注册资本100 万人民币，公司位于三亚市，许可经营项目：技术进出口、货物进出口（许可经营项目凭许可证件经 营）一般经营项目：通用零部件制造、汽车零部件及配件制造、汽车零部件再制造、机械零件、零部件 加工、机械零件、零部件销售、汽车零部件研发、以自有资金从事投资活动（经营范围中的一般经营项 目依法自主开展经营活动，通过国家企业信用信息公示系统（海南）向社会公示）。 ...

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]

Core Viewpoint - The intelligent driving industry is entering a commercialized era, with the approval of L3-level autonomous driving marking a shift from technical validation to commercial application, enhancing expectations for policy, industry, and performance transmission [1] Industry Developments - Recent key policy breakthroughs and industry advancements in intelligent driving include the approval of China's first L3-level autonomous driving vehicles and Tesla's initiation of unmanned Robotaxi road tests, indicating a transition to large-scale application [1] - The intelligent driving sector is becoming a core engine for the transformation and upgrading of the automotive industry, fostering a collaborative ecosystem across the entire industry chain [1] Market Performance - In the secondary market, Zhejiang Shibao, focusing on steer-by-wire technology, recorded a maximum increase of 96.8% within the month, while Wanji Technology, focusing on lidar technology, saw a maximum increase of 52.7% [1] Company Insights - **Wanji Technology**: The company’s 128-line lidar has received approval from a major passenger vehicle manufacturer, and its 192-line lidar has passed validation from multiple mainstream automakers. The lidar products are being applied in leading commercial vehicles like Robobus [8] - **Zhejiang Shibao**: As a leader in electric power steering systems, the company has established long-term partnerships with several domestic traditional and new energy vehicle manufacturers, indicating a robust order book and normal operations [6][7] - **Bertley**: A leader in the automotive brake sector, the company holds a 12.59% market share in electronic parking brake systems in China and has received awards for its innovative small-diameter caliper technology, which enhances vehicle performance [7] - **Yongxin Optical**: The company specializes in lidar optical components and is expected to ship nearly one million units by the first half of 2025, with strong partnerships with leading lidar manufacturers [8]

Investment Summary - The automotive industry is benefiting from the acceleration of smart technology and the development of the robotics industry, with the parts sector outperforming the vehicle sector. From January 1 to December 12, 2025, the CITIC passenger car index fell by 0.40%, while the CITIC automotive parts index rose by 34.76%, indicating a significant difference in performance between the two sectors [4][18][25]. Group 1: 2025 Market Performance and Earnings Review - The automotive parts sector achieved a revenue of 7,541.60 billion yuan in the first three quarters of 2025, a year-on-year increase of 8.75%, and a net profit of 460.10 billion yuan, up 19.60% year-on-year [49]. - The passenger vehicle sector's revenue reached 15,203.16 billion yuan, growing by 8.68% year-on-year, while the net profit decreased by 15.72% to 391.90 billion yuan [31][49]. - The performance of passenger vehicle companies varied, with most showing revenue growth, but some, like BYD and Great Wall Motors, experienced profit declines [39][42]. Group 2: Outlook for 2026 - The automotive market in 2026 is expected to see a decline in policies, while exports and new energy vehicles (NEVs) will continue to rise. The "old-for-new" policy is anticipated to drive high growth in vehicle sales in 2025, but its absence in 2026 may lead to a demand shortfall [5][62][66]. - The penetration rate of NEVs is expected to continue increasing, with smart and high-end vehicles becoming new growth drivers. By 2025, the penetration rate of NEVs reached 46.7% [72][73]. - The L3 commercial application is expected to reach a critical point in 2026, with smart chassis technology anticipated to be applied in large quantities [5][6]. Group 3: Investment Strategy - The investment strategy focuses on the smart automotive sector, particularly as the industry transitions from L2 to L3 autonomous driving. Companies that continue to invest in this area are expected to benefit significantly [6][8]. - Recommended companies in the vehicle sector include SAIC Motor, Jianghuai Automobile, and Chery Automobile, which are positioned to leverage advancements in smart driving technology [6][8]. - In the parts sector, companies like Baolong Technology and Top Group are highlighted for their potential to benefit from the implementation of line control steering and braking systems, which are set to enter mass application in 2026 [8][49].

Core Viewpoint - Ningbo Gaofa (603788) announced the election of Mr. Tu Yimin as the employee representative director of the company's fifth board of directors during the employee representative conference scheduled for November 18, 2025 [1] Company Summary - Ningbo Gaofa will hold an employee representative conference on November 18, 2025 [1] - Mr. Tu Yimin has been elected as the employee representative director [1]

本公司董事会及全体董事保证本公告内容不存在任何虚假记载、误导性陈述 或者重大遗漏，并对其内容的真实性、准确性和完整性承担法律责任。 证券代码：603788 证券简称：宁波高发 公告编号：2025-043 宁波高发汽车控制系统股份有限公司 关于非独立董事辞职暨选举职工代表董事的公告 二、关于选举职工代表董事的情况 根据证监会发布的《关于新<公司法>配套制度规则实施相关过渡期安排》及 《公司章程》的规定，公司董事会设职工代表董事一名。公司于 2025 年 11 月 18 日召开 2025 年第一次职工代表大会，经与会职工代表审议，同意选举屠益民 先生（简历详见附件）为公司第五届董事会职工代表董事，任期自本次职工代表 大会审议通过之日起至公司第五届董事会任期届满之日止。 重要内容提示： 董事会近日收到非独立董事屠益民先生提交的书面辞职报告。因公司治 理结构调整，屠益民先生申请辞去公司第五届董事会非独立董事职务，其辞职报 告自送达公司董事会之日起生效。除辞任非独立董事职务外，屠益民先生在公司 担任的其他职务不变。 公司于 2025 年 11 月 18 日召开职工代表大会，选举屠益民先生为公司第 五届董事会职工代表董事 ...