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 ...

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 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： 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，任期与公司第五届董事会任期一致。 本次选举完成后，公司董事会中兼任公司高级管理人员职务的董事以及由职 工代表担任的董事，总计未超过公司董事总数的二分之一。 特此公告。 一、董事离任情况 (一) 提前离任的基本情况 | 姓名 | 离任职务 | 离任时间 | | 原定任期 | | | 是否继续在上 | | 是否存在未 | | --- | --- | --- | --- | --- | -- ...

宁波高发汽车控制系统股份有限公司 2025年第一次临时股东大会决议公告 证券代码：603788 证券简称：宁波高发 公告编号：2025-042 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： 本次会议是否有否决议案：无 一、 会议召开和出席情况 (三) 出席会议的普通股股东和恢复表决权的优先股股东及其持有股份情况： | 1、出席会议的股东和代理人人数 | 84 | | --- | --- | | 2、出席会议的股东所持有表决权的股份总数（股） | 115,106,080 | | 3、出席会议的股东所持有表决权股份数占公司有表决权股 | | | 份总数的比例（%） | 51.6020 | (四) 表决方式是否符合《公司法》及《公司章程》的规定，大会主持情况等。 本次会议采取现场与网络投票相结合的方式进行记名投票表决，对需审议议 案进行逐项投票表决，表决方式符合《公司法》、《公司章程》及国家相关法律、 法规的规定。公司董事长钱高法先生主持本次股东大会。 (五) 公司董事、监事和董事会秘书的出席情况 1、 公司在任董 ...

上海市锦天城律师事务所 关于宁波高发汽车控制系统股份有限公司 2025 年第一次临时股东大会的 上海市锦天城律师事务所 关于宁波高发汽车控制系统股份有限公司 2025 年第一次临时股东大会的 法律意见书 法律意见书 地址：上海市浦东新区银城中路 501 号上海中心大厦 9/11/12 层 电话：021-20511000 传真：021-20511999 邮编：200120 上海市锦天城律师事务所 法律意见书 上海市锦天城律师事务所 法律意见书 本次股东大会的现场会议于 2025 年 11 月 18 日 13 点 30 分在浙江省宁波市 鄞州区下应北路 717 号公司会议室召开。通过上海证券交易所交易系统进行的网 络投票时间为：2025 年 11 月 18 日上午 9:15-9:25，9:30-11:30，下午 13:00-15:00； 通过上海证券交易所互联网投票的时间为 2025 年 11 月 18 日 9:15-15:00 期间。 本所律师审核后认为，本次股东大会召集人资格合法、有效，本次股东大 会召集、召开程序符合《公司法》《上市公司股东会规则》等法律、法规、规 章和其他规范性文件以及《公司章程》的有关规 ...

Core Viewpoint - Ningbo Gaofa Automotive Control System Co., Ltd. is set to hold its first extraordinary general meeting of shareholders in 2025 on November 18, 2025, to discuss seven proposals, including one special resolution that requires a two-thirds majority for approval [2][4]. Meeting Details - The meeting will take place on November 18, 2025, at 13:30 in the company's conference room located at 717 Xiaying North Road, Yinzhou District, Ningbo, Zhejiang Province [3][13]. - Voting will be conducted through a combination of on-site and online methods, utilizing the Shanghai Stock Exchange's online voting system [2][3]. Voting Procedures - The online voting will be available on the day of the meeting from 9:15 to 15:00, with specific time slots for trading system voting [2][3]. - Shareholders can vote through either the trading system or the internet voting platform, with the requirement of identity verification for first-time users [4][5]. Proposals for Discussion - A total of seven proposals will be reviewed, with one being a special resolution and the others ordinary resolutions requiring a simple majority for approval [2][4]. - The proposals have been previously approved by the company's board and supervisory board meetings held on October 28, 2025 [4]. Attendance and Registration - Shareholders registered with the China Securities Depository and Clearing Corporation Limited are eligible to attend the meeting and can appoint proxies to vote on their behalf [8][9]. - Registration for attendance requires specific documentation, including identification and proof of shareholding [12][13]. Additional Information - The meeting is expected to last half a day, and attendees will be responsible for their own travel and accommodation expenses [14]. - Contact information for inquiries regarding the meeting is provided, including names and phone numbers of company representatives [16].