关于延长长城证券股份有限公司 2025 年面向专业投资者公开发 长城证券股份有限公司 年 月 日 （本页无正文，为《关于延长长城证券股份有限公司 2025 年面向专业投资者公 开发行次级公司债券（第二期）簿记建档时间的公告》之盖章页） 南京证券股份有限公司 年 月 日 （本页无正文，为《关于延长长城证券股份有限公司 2025 年面向专业投资者公 开发行次级公司债券（第二期）簿记建档时间的公告》之盖章页） 北京市嘉源律师事务所 年 月 日 根据《长城证券股份有限公司 2025 年面向专业投资者公开发行次级公司债 券（第二期）发行公告》，发行人和簿记管理人定于 2025 年 11 月 5 日 15:00-18:00 以簿记建档的方式向网下投资者进行利率询价，并根据簿记建档结果确定本期债 券的最终票面利率。 因簿记建档当日市场变化，经发行人、簿记管理人及其他簿记参与方协商一 致，现将簿记建档结束时间由 2025 年 11 月 5 日 18:00 延长至 19:00。 特此公告。 （本页无正文，为《关于延长长城证券股份有限公司 2025 年面向专业投资者公 开发行次级公司债券（第二期）簿记建档时间的公告》之盖章页） ...

Group 1: Financial Performance - The company achieved a total revenue of 22.86 billion yuan in the first three quarters of 2025, representing a year-on-year increase of 41.21% [2] - The total profit reached 19.31 billion yuan, with a year-on-year growth of 75.83% [2] - Basic earnings per share increased to 0.48 yuan, reflecting a growth of 77.88% compared to the previous year [2] Group 2: Business Strategy - The company emphasizes a strategic focus on "safety," "leading," and "service" to guide its operations [3] - The development strategy includes a commitment to "industry brokerage + first-class investment banking," aiming to strengthen capital-intensive businesses while expanding light capital operations [3] Group 3: Customer Acquisition and Management - The company reported a significant year-on-year increase in new customer acquisition, leveraging market opportunities [3] - A three-step approach ("active customers, frequency enhancement, conversion") is employed to improve the conversion rate and activity of effective accounts [3] - The wealth management business adopts a "customer-centric" philosophy, integrating research, investment, and advisory services to enhance customer engagement and value conversion [3]

Core Points - Great Wall Securities (002939) stated on November 4 that the total number of shareholders is expected to reach 138,334 by October 31, 2025 [1]

| 发行人: | 长城证券股份有限公司 | | --- | --- | | 主承销商: | 南京证券股份有限公司 | | 受托管理人: | 南京证券股份有限公司 | | 本期发行金额: | 不超过20亿元(含) | | 增信措施情况: | 本期债券无担保 | | 信用评级结果: | 发行人主体信用等级 AAA,债券信用等级 AA+ | | 信用评级机构: | 联合资信评估股份有限公司 | 长城证券股份有限公司 2025 年面向专业投资者公开发行次级公司债券（第二期） 募集说明书 声明 本公司将及时、公平地履行信息披露义务，本公司及其全体董事、高级管理 人员或履行同等职责的人员保证募集说明书信息披露的真实、准确、完整，不存 在虚假记载、误导性陈述或重大遗漏。 主承销商已对募集说明书进行了核查，确认不存在虚假记载、误导性陈述和 重大遗漏，并对其真实性、准确性和完整性承担相应的法律责任。 本公司承诺本期债券合规发行，不从事《关于进一步规范债券发行业务有关 事项的通知》（深证上[2024]38 号）第三条第二款的相关规定，本公司不得直接 或者间接认购自己发行的债券。本公司不得操纵发行定价、暗箱操作；不得以代 持、信托 ...

长城证券股份有限公司 2025 年面向专业投资者公开发行次级公司债券 （第二期）发行公告 | 注册金额： | 不超过人民币 50 | 亿元（含 | 亿元） 50 | | --- | --- | --- | --- | | 本期发行金额： | 不超过人民币 20 | 亿元（含 | 20 亿元） | | 增信情况： | 本期债券无担保 | | | | 资信评级机构： | 联合资信评估股份有限公司 | | | | 发行人主体信用等级： | AAA | | | | 本期债券信用等级： | AA+ | | | 发行人 主承销商/簿记管理人 签署日期：2025 年 11 月 4 日 本公司及董事会体成员保证公告内容的真实、准确和完整，对公告的虚假 记载、误导性陈述或者重大遗漏负连带责任。 重要提示 1、长城证券股份有限公司（以下简称"发行人"或"公司"）面向专业投资者 公开发行面值总额不超过人民币 50 亿元公司债券，已获得中国证券监督管理委 员会证监许可〔2024〕974 号核准。 发行人本次债券采取分期发行的方式，本期发行为本次债券下第四期发行， 为长城证券股份有限公司 2025 年面向专业投资者公开发行次级公司债券 ...

长城证券股份有限公司 2025 年面向专业投资者公开发行 次级公司债券(第二期) 信用评级报告 联合资信评估股份有限公司 China Lianhe Credit Rating Co.,Ltd. www.lhratings.com 专业 | 尽责 真 诚 服务 - - 信用评级公告 联合(2025) 10576 号 联合资信评估股份有限公司通过对长城证券股份有限公司及其 拟面向专业投资者公开发行的 2025年次级公司债券(第二期)的信 用状况进行综合分析和评估,确定长城证券股份有限公司主体长期 信用等级为 AAA,长城证券股份有限公司 2025年面向专业投资者 公开发行次级公司债券(第二期)信用等级为 AA*,评级展望为稳 定。 特此公告 联合资信评估股份有 评级总监: 二〇二五年十月二十四日 Add: 17/F, PICC Building, 2, Jianguomenwai Street, Beijing PRC:1000 地址: 北京市朝阳区建国门外大街2号PICC大厦17层 邮编:100022 电话(Tel) : (010) 85679696 | 传真(Fax):(010)85679228 | 邮箱(E ...

2024 年 6 月 21 日，中国证券监督管理委员会以证监许可【2024】974 号文 同意长城证券股份有限公司面向专业投资者公开发行次级公司债券。 长城证券股份有限公司 2024 年面向专业投资者公开发行次级公司 债券更名公告 由于债券分期发行且涉及跨年，按照公司债券命名惯例，征得主管部门同意， 本期债券名称由"长城证券股份有限公司 2024 年面向专业投资者公开发行次级 公司债券"变更为"长城证券股份有限公司 2025 年面向专业投资者公开发行次 级公司债券（第二期）"。本期债券分为两个品种，品种一全称为"长城证券股份 有限公司 2025 年面向专业投资者公开发行次级公司债券（第二期）（品种一）"， 债券简称为"25 长城 C3"；品种二全称为"长城证券股份有限公司 2025 年面向 专业投资者公开发行次级公司债券（第二期）（品种二）"，债券简称为"25 长城 C4"。 (本页无正文，为《长城证券股份有限公司 2024 年面向专业投资者公开发行次 级公司债券更名公告》之签章页 ) 本期债券名称变更不改变原签订的与本期公司债券发行相关的法律文件效 力，原签署的相关法律文件对更名后的公司债券继续具有法律效力 ...

Core Insights - The securities industry is entering a deep transformation phase towards digital intelligence, with large model technology providing revolutionary opportunities for wealth management business [1][2] - The application of large models in the securities industry has transitioned from experimental stages to commercial implementation, driven by increasing wealth management demand and various transformation pressures [2][3] Industry Trends - Wealth management is shifting from generic financial sales to differentiated marketing focused on customer experience [4] - The integration of online and offline services is leading to a more connected operational model in wealth management [4] - The industry is moving towards intelligent and precise wealth management, utilizing big data for targeted customer identification and marketing [4] Challenges Faced - High customer acquisition costs, with online costs per effective account rising to 300-400 yuan, and some premium channels exceeding 1000 yuan [5] - Weak data governance, with only 1%-2% of IT investment allocated to data management, leading to issues of data inconsistency and quality [5] - Insufficient advisory capabilities, as wealth management transformation demands higher professional skills from advisors [5] - High service costs, with traditional models requiring advisors to serve nearly 3000 clients each, hindering personalized service [5] Opportunities from Large Models - Large model technology enhances efficiency through intelligent reports, content understanding, and customer service, improving service quality and operational efficiency [6] - Cost optimization is achieved via automation, intelligent recommendations, and precise marketing, reducing acquisition and service costs [6] - Capability enhancement through knowledge bases and reasoning chains addresses the professional skill gaps in advisory teams [6] Application Framework - The infrastructure layer includes computing and storage resources, with leading firms utilizing high-performance GPU clusters while smaller firms may share resources [8] - The model layer consists of general and finance-specific models, with a mixed architecture approach to balance specialization and cost [9] - The application technology layer connects models to business scenarios, utilizing RAG technology, prompt engineering, and intelligent agent technology [10] Implementation Path - The implementation of large model applications should follow a phased strategy: infrastructure development, core capability enhancement, and business scenario penetration [14] - Leading firms adopt a "self-research first, cooperation second" strategy, while smaller firms focus on rapid application of general model APIs [15] Recommendations for Development - Firms should choose appropriate technology paths based on their resources, with larger firms investing in self-research and smaller firms leveraging open-source models [17] - Focus on high-frequency, essential business scenarios for application, such as intelligent customer service and risk control [17] - Strengthening data governance is crucial to ensure data quality and compliance for large model applications [17] - Investment in training financial technology talent is necessary to support innovation in the sector [17]

Core Insights - The "Second Jin Qilin Best Investment Advisor Selection" event is actively seeking outstanding investment advisors to enhance wealth management and build a collaborative platform [1][12] - The event features various competitions, including stock simulation trading and public fund simulation allocation, with a focus on identifying top-performing advisors [1] ETF Group Rankings - In the ETF group for October, Nanjing Securities' Zhang Yao achieved a return of 13.6%, ranking first, followed by Dongxing Securities' Ge Qi and Liu Qianjun in second and third places respectively [1] Fund Group Rankings - The top performer in the fund group for October was Guangda Securities' Zhou Jianhua with a return of 8.1%, while Zhongjin Wealth's Bai Jiangbo and Bank of China Securities' Ling Tonglong secured the second and third positions [1] Stock Simulation Trading Rankings - The top three in the stock simulation trading for October are: - Liu Zili from China Merchants Securities with a return of 19.79% - Zhou Juan from Yintai Securities with a return of 18.95% - Quan Wendong from Changjiang Securities with a return of 17.35% [1][2][7]

Company Overview - Heshun Technology (SZ 301237) announced an investor research meeting scheduled for October 31, 2025, where the company’s board secretary and CFO will participate in addressing investor inquiries [1] - As of the report, Heshun Technology has a market capitalization of 3.8 billion yuan [1] Revenue Composition - For the year 2024, Heshun Technology's revenue composition is as follows: 97.66% from rubber and plastic products, and 2.34% from other businesses [1]