Group 1 - The evaluation results for the information disclosure work of listed companies in Shenzhen for the 2024 to 2025 period have been released, covering 16 listed brokerages and their main bodies [1] - Among the evaluated companies, 9 received an A rating, including Shenwan Hongyuan, Guoyuan, Guohai, GF Securities, Changjiang, Guoxin, First Capital, Great Wall, and Dongfang Caifu [1] - 6 companies received a B rating, which are Northeast, Guosheng Jinkong, Western, Huaxi, Shanxi, and Hualin [1] - Only 1 company received a C rating, which is Jinlong Co., Ltd [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 ...

Group 1 - The core issue is that Das Intelligente has received a notice of investigation and management from the Ying Shang County Supervisory Committee, involving its actual controller and chairman Liu Bang, who is under investigation [1] - As of the announcement date, the company has not been informed of the progress or conclusion of the investigation, but it has arranged relevant work properly [1] - The company's control has not changed, and other directors and senior management are performing their duties normally, with no significant adverse impact on daily operations [1] Group 2 - In the first half of 2025, Das Intelligente reported a revenue of 990 million yuan, a year-on-year decrease of 26.80% [2] - The net profit attributable to shareholders was -88.76 million yuan, compared to a profit of 9.31 million yuan in the same period last year [2] - The net cash flow from operating activities was -285 million yuan, slightly improved from -291 million yuan in the previous year [2] Group 3 - Das Intelligente was established in 1995 and is located in Shenzhen, Guangdong Province, primarily engaged in software and information technology services [3] - The registered capital of the company is 2,120.58 million yuan, with the same amount for paid-in capital [3]

Group 1 - The three major indices opened lower, with the CSI All Share Securities Company Index down 0.39%, while some stocks like Nanjing Securities and First Capital rose, and others like Xiangcai Securities fell nearly 1% [1] - The Broker ETF (159842) also saw a decline of 0.42%, with a trading volume exceeding 17 million yuan and a premium rate of 0.07%, indicating initial premium activity [1] - The Broker ETF has experienced net inflows for three consecutive trading days, accumulating nearly 100 million yuan in total [1] Group 2 - According to a report by Huatai Securities, the capital market is undergoing profound changes, with a low interest rate environment significantly enhancing the attractiveness of equity asset allocation, suggesting a positive development cycle ahead [2] - The brokerage sector is highly correlated with capital market activities, and there is optimism regarding its performance growth potential and value recovery in the new cycle [2] - Current valuations for the sector in both A and H shares remain at mid to low levels, with a focus on selecting stocks with better valuations in Hong Kong, leading A-share companies with valuation advantages, and specialized mid-sized brokerages [2]

Core Viewpoint - The active trading in the margin financing and securities lending (referred to as "two financing") market has led to significant growth in net interest income for listed brokerages, prompting many to raise their business scale limits to meet market demand [1][2][3]. Group 1: Business Growth and Market Demand - Several brokerages, including Huatai Securities and China Merchants Securities, have announced increases in their two financing business scale limits, with Huatai's limit set to three times its net capital and China Merchants increasing its limit from 150 billion to 250 billion [2][3]. - The two financing market has seen a substantial increase in balance, reaching 2.49 trillion yuan as of October 31, with a year-on-year growth of 33.34% [3][4]. - The number of new two financing accounts opened in September reached 205,400, marking a record high for the year [3]. Group 2: Revenue Growth for Brokerages - In the first three quarters of the year, 42 listed brokerages achieved a total net interest income of 33.906 billion yuan, reflecting a year-on-year increase of 54.52% [4]. - Among these brokerages, 30 reported a year-on-year increase in net interest income, with notable growth rates from Longcheng Securities (3126.77%) and Guotai Junan (232.31%) [4]. Group 3: Strategic Enhancements and Compliance - Brokerages are enhancing their service capabilities and market share in the two financing sector, with companies like Guoyuan Securities focusing on risk management and Southwest Securities leveraging financial technology to improve service efficiency [5]. - It is emphasized that brokerages must balance growth with compliance and safety, ensuring they meet regulatory requirements while expanding their two financing operations [5].

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]

Quantitative Models and Factor Construction Quantitative Models and Construction Methods 1. Model Name: Broker Gold Stock Performance Enhancement Portfolio - **Model Construction Idea**: The model aims to optimize the selection from the broker gold stock pool to outperform the benchmark index of equity-biased hybrid funds[12][39] - **Model Construction Process**: - The model uses the broker gold stock pool as the stock selection space and constraint benchmark - It employs portfolio optimization to control deviations in individual stocks and styles from the broker gold stock pool - The industry allocation is based on the industry distribution of all public funds - The portfolio is adjusted at the closing price on the first day of each month[12][39][42] - **Model Evaluation**: The model has shown stable performance historically, consistently outperforming the equity-biased hybrid fund index annually from 2018 to 2022[12][39][42] Model Backtest Results Broker Gold Stock Performance Enhancement Portfolio - **Absolute Return (Monthly)**: -0.77% (20251009-20251031)[41] - **Excess Return Relative to Equity-biased Hybrid Fund Index (Monthly)**: 1.37% (20251009-20251031)[41] - **Absolute Return (Year-to-date)**: 35.08% (20250102-20251031)[41] - **Excess Return Relative to Equity-biased Hybrid Fund Index (Year-to-date)**: 2.61% (20250102-20251031)[41] - **Ranking in Active Equity Funds (Year-to-date)**: 40.13% percentile (412/3469)[41] Quantitative Factors and Construction Methods 1. Factor Name: Total Market Value - **Factor Construction Idea**: This factor measures the total market capitalization of a stock, which is often used to capture the size effect in stock returns[3][28] - **Factor Construction Process**: - The total market value is calculated as the product of the stock's current price and the total number of outstanding shares[3][28] - **Factor Evaluation**: The total market value factor has shown good performance in the recent month and year-to-date periods[3][28] 2. Factor Name: Single Quarter Revenue Growth Rate - **Factor Construction Idea**: This factor measures the growth rate of a company's revenue in a single quarter, indicating its short-term growth potential[3][28] - **Factor Construction Process**: - The single quarter revenue growth rate is calculated as the percentage change in revenue from the previous quarter to the current quarter[3][28] - **Factor Evaluation**: The single quarter revenue growth rate factor has shown good performance year-to-date[3][28] 3. Factor Name: Analyst Net Upward Revision - **Factor Construction Idea**: This factor measures the net number of upward revisions by analysts, reflecting positive changes in analyst sentiment[3][28] - **Factor Construction Process**: - The analyst net upward revision is calculated as the difference between the number of upward revisions and the number of downward revisions over a specific period[3][28] - **Factor Evaluation**: The analyst net upward revision factor has shown good performance year-to-date[3][28] Factor Backtest Results Total Market Value Factor - **Recent Month Performance**: Good[3][28] - **Year-to-date Performance**: Good[3][28] Single Quarter Revenue Growth Rate Factor - **Recent Month Performance**: Not specified - **Year-to-date Performance**: Good[3][28] Analyst Net Upward Revision Factor - **Recent Month Performance**: Not specified - **Year-to-date Performance**: Good[3][28]