平台上线以来，信息处理效率大幅提升，跨部门知识共享的壁垒逐步消解，投研团队得以将更多精力投 入到策略设计与深度分析等核心环节，实现资源向高附加值工作的高效倾斜。这一效率的提升，不仅让 内部沟通更高效，也推动公司逐步建立起智能化、可共享的知识管理体系，让知识真正变成可复用的资 产。 在人工智能技术加速重构金融业态的今天，一场由大模型驱动的投研生态变革正全面提速，迈向深度智 能化的新阶段。面对海量研报、会议纪要等非结构化数据"沉睡"、知识复用难等行业痛点，平安基金率 先破局，正式推出自主研发的"AI青蚨"——一个深度适配基金投研场景的智能知识引擎。 该平台不仅将研报分析效率从数小时缩短至分钟级，更通过智能标注、跨团队共享与人机协同机制，重 构投研流程，降低知识流转成本，标志着公司在AI深度应用领域迈入自主可控的新阶段。在头部机构 纷纷布局智能投研的竞赛中，平安基金正以技术自研为支点，撬动投研体系的智能化升级，加速抢占 AI投研时代的制高点。 应势而生：从破解"最后一公里"难题到构建智能知识中枢 当前，全球头部金融机构正加速布局大模型驱动的智能投研体系。然而，尽管生成式AI技术迭代迅 速，投资研究领域仍面临非结构化 ...

Group 1: Hong Kong Stock Market Financing - The Hong Kong stock market has seen active financing since the beginning of the year, with equity financing reaching HKD 39.09 billion, a year-on-year increase of 316.27% [1] - In 2025, the equity financing scale in the Hong Kong primary market reached HKD 612.7 billion, up 248.8% year-on-year [1] - Chinese securities firms are increasingly dominating the equity underwriting business, holding six of the top ten positions in underwriting amounts, with a combined market share of 56.15% [1] - CICC led the IPO sponsorship with an underwriting scale of HKD 51.65 billion, followed by CITIC Securities (Hong Kong) with HKD 46.03 billion [1] - The active financing environment is expected to enhance the revenue outlook for securities firms and boost the valuation recovery of the securities sector [1] Group 2: Changes in Western Securities Research Department - Mu Qiguo has resigned as the head of the research institute at Western Securities, with Zhao Yinghua, the vice president in charge of the research institute, taking over [2] - The change reflects the higher demands for research capabilities under the company's "14th Five-Year" strategic plan, indicating a focus on long-term development and resource optimization [2] - Mu Qiguo will focus on intelligent investment research and the establishment of the Hong Kong subsidiary's research business, which may enhance the competitiveness of the brokerage sector in digital transformation [2] Group 3: Insurance Asset Management Registration - In 2025, insurance asset management institutions registered 314 debt investment plans, equity investment plans, and private equity funds, with a total scale of CNY 510.44 billion, showing a year-on-year decrease of 20.71% in quantity and 26.08% in scale [3] - The number of debt investment plans decreased significantly, which may impact financing in infrastructure and related sectors [3] - The growth in equity investment plans, with a 12.52% increase in scale, indicates a shift of insurance capital towards equity markets, positively influencing the long-term investment environment [3]

Group 1 - The Hong Kong stock market has seen a significant increase in equity financing activities, with a total of HKD 390.9 billion raised as of January 19, 2025, representing a year-on-year growth of 316.27% [1] - In 2025, the total equity financing scale in the Hong Kong primary market reached HKD 6,127 billion, showing a year-on-year increase of 248.8% [1] - Chinese securities firms are increasingly dominating the equity underwriting business in the Hong Kong market, holding six out of the top ten positions in underwriting amounts, with a combined market share of 56.15% [1] Group 2 - Mu Qiguo has stepped down as the head of the research institute at Western Securities, with Zhao Yinghua taking over the role [2] - The leadership change reflects the updated requirements of the company's "14th Five-Year" strategic plan for research and development capabilities [2] - Mu Qiguo will focus on the company's intelligent investment research and the establishment of research operations for its Hong Kong subsidiary [2] Group 3 - In 2025, insurance asset management institutions registered a total of 314 debt and equity investment plans and private equity funds, with a total scale of CNY 510.443 billion, showing a year-on-year decrease of 20.71% in quantity and 26.08% in scale [3] - The number of debt investment plans decreased significantly to 285, with a scale of CNY 441.905 billion, reflecting a year-on-year decline of 24% and 28.46% respectively [3] - Conversely, the number of equity investment plans increased to 22, with a scale of CNY 33.532 billion, indicating a year-on-year growth of 83.33% in quantity and 12.52% in scale [3]

Group 1 - Mu Qiguo has resigned as the head of the Research Institute of Western Securities, with Zhao Yinghua, the deputy general manager in charge of the research institute, taking over the role [2] - The company stated that the new development phase requires updated and higher capabilities for the research center's business development, market expansion, business innovation, and internal collaboration [2] - Mu Qiguo will focus on the company's intelligent investment research and the establishment of research operations for its Hong Kong subsidiary [3] Group 2 - During Mu Qiguo's tenure, his team won the New Fortune Most Potential Research Institution Award for three consecutive years from 2019 to 2021 and was recognized as the Most Characteristic Research Institution by Sina Golden Unicorn in 2020 and 2021 [3] - Mu Qiguo emphasized that smaller brokerages can focus on niche areas with significant Beta changes and upstream industry research to provide unique insights, which can yield higher Alpha value for investors [6] - The research development center's revenue and operating profit have significantly increased year-on-year since 2025, with insurance client revenue surpassing public funds to become the main source of income [7]

Group 1 - The core point of the article is the resignation of Mu Qiguo as the head of the Research Institute of Western Securities, with Zhao Yinghua taking over the role. This change aligns with the company's updated strategic planning for its research center, emphasizing higher demands for business development and innovation [2][3]. - Mu Qiguo will focus on the company's intelligent investment research and the establishment of its Hong Kong subsidiary's research operations [3]. - Western Securities aims to enhance its research capabilities by strengthening sell-side research, expanding overseas business, and fostering technological innovation to better serve investors [3][5]. Group 2 - During Mu Qiguo's tenure, his team received multiple awards, including the New Fortune Most Promising Research Institution Award for three consecutive years from 2019 to 2021 and the Most Distinctive Research Institution Award from Sina Jin Qilin in 2020 and 2021 [3]. - Mu Qiguo emphasized the potential for smaller brokerages to focus on niche markets with significant beta changes and upstream industry research to provide unique insights, which can yield higher alpha value for investors [4]. - Under Mu Qiguo's leadership, the research development center saw significant growth in revenue and profit, with insurance clients becoming the primary revenue source by surpassing public funds in 2025 [5].

与市场上常见的工具集合不同，"智研多资产配置+"平台通过三大有机联动的核心体系， 构建了一个完整的智能投研价值闭环。 深度研究工具库是系统的"认知基石"。它超越了传统的数据罗列，构建了穿透式的资产洞 察网络。其中，涵盖5 0 0多个深度标签的基金特色分析库，为每只基金绘制精准画像；而 颇 具 特 色 的 " 前 沿 声 音 " 智 库 ， 则 借 助 AI 智 能 听 录 技 术 ， 将 数 百 场 基 金 经 理 访 谈 及 内 部 路 演，转化为可秒级检索、结构化的观点数据库，让隐性知识得以显性化沉淀与运用。 量化配置模型库是平台的"决策引擎"。它将宏观研判与中观战术从定性讨论推进至定量指 引。平台集成了美林时钟、货币信用周期等经典资产配置模型，同时融合了具有中信建投 特色的研究成果，如改进的普林格周期模型、宏观状态择时模型等，能够将复杂的宏观环 境转化为清晰的配置信号。行业轮动模型基于因子动量的思想，涵盖宏观、基本面、资金 面、事件动量等七大子维度，共同构造复合的行业轮动策略，提供从宏观到中观的完整决 策链。 AI增强是提效的"加速器"。平台引入了AI智能体矩阵，将人工智能技术深度植入投研全流 程。7× ...

数据处理的"超级大脑"。以分析企业财务状况为例，它能快速从海量数据中提取关键指标，经横纵 向对比分析后生成直观图表报告，助研究员明晰企业财务健康度与发展趋势。同时，实时监测市场动 态，及时预警潜在投资机会与风险，为决策提供支撑。 信息整合的"智慧中枢"。面对信息分散的行业痛点，慧博投研的核心突破在于将碎片化的信息采集 转化为一体化的智能工作流。通过合规的技术手段，系统能够从多元信息源自动汇聚数据，无论是公众 号内容、外网资源还是专业数据库，都能被有机整合到统一的投研平台中。 近日，慧博投研与中信证券、中金公司两家行业标杆券商达成合作，为其量身打造投研智能体平 台，推动数据与人工智能技术在金融投研领域的深度融合。 当下，投资机构升级数字化战略、向智能化转型已成必然。慧博投研此举为中信、中金两家券商提 供了有力支撑，助力其稳固核心竞争力，推动金融机构迈向高质量发展新阶段。 破解之道：慧博智能体驱动投研全面升级 处理海量数据、精准筛选信息、实现高效决策，是当前金融投研领域有待攻克的瓶颈之一。破解这 些难题，是提升金融投研效率、驱动价值重塑的关键所在。 在服务券商的实践中，慧博投研发现，客户真正的痛点往往不是缺少信息 ...

Core Viewpoint - AI technology is reshaping the asset management industry by enhancing research capabilities and operational efficiency, transitioning from a human-driven model to a dual-driven model of human intelligence and machine intelligence [1][6]. Group 1: AI Integration in Asset Management - The asset management industry is data and knowledge-intensive, and AI's characteristics align well with its needs, enabling significant improvements in research efficiency and investment capabilities [2][4]. - Taikang Asset has developed its own deep research agent (TKDR) that integrates extensive internal and external research data, enhancing the investment research process [2][4]. Group 2: Advantages of TKDR - TKDR demonstrates advantages over traditional research methods by quickly identifying core demands, utilizing resources efficiently, and producing structured research outputs [3][5]. - The system is designed to support active investment research, aligning with the methodologies of active researchers and enhancing the quality and timeliness of outputs [5]. Group 3: Future Outlook and Strategic Development - The construction of an intelligent agent system is seen as a key driver for AI development, with plans to create a collaborative human-machine research paradigm [6][8]. - The transition to AI-driven asset management is expected to enhance decision-making across various functions, including investment strategy, risk management, and operational efficiency [6][7]. Group 4: Recommendations for AI Implementation - To effectively advance AI initiatives, asset management firms should establish supportive mechanisms, optimize governance structures, and develop talent teams that align with AI transformation [7][8]. - Emphasizing collaboration between business and technology teams is crucial for successful AI integration, ensuring that AI applications are closely aligned with business objectives [8].

Group 1 - The "6th 1024 Asset Management Technology Developers Conference (ITDC 2025)" was held in Shanghai, marking a significant event in the "Global Asset Management Center Shanghai International Activity Week 2025" series [1] - The report titled "2025 Report on the Development and Application of Large Models in the Asset Management Vertical Field of Shanghai Global Financial Technology Center" was officially released, showcasing the application of domestic large models in asset management with a focus on both technical depth and industry practice [1] - The "Drip Intelligence" initiative for intelligent investment research and AI + industrial development was launched, aiming to create a permanent platform for "industry research + scenario roadshows + closed-door discussions + joint initiatives" focusing on key sectors such as smart vehicles, high-end equipment, integrated circuits, civil aviation, and the digital economy [1] Group 2 - A pre-conference meeting was held with over 60 experts from various organizations, including the Shanghai Municipal Financial Office and the Lingang New Area Management Committee, discussing the core goal of building a benchmark financial technology cluster [2] - Key topics of discussion included industrial collaboration, financing development, cross-border data flow, computing power infrastructure, and offshore financial scenarios, aimed at promoting the aggregation of financial technology resources, technological innovation, and industry implementation [2]

Core Viewpoint - The sixth Asset Management Technology Developers Conference emphasizes the deep integration of AI in the asset management industry, highlighting the historical opportunities presented by AI technologies [2][3]. Group 1: Conference Overview - The conference was held on October 17, 2025, in Shanghai, focusing on the theme "AI + Asset Management," gathering over 300 guests from various sectors including government, finance, and technology [2][4]. - The event is part of the "Global Asset Management Center Shanghai International Activity Week 2025" and aims to explore the application and development trends of AI in asset management [2][4]. Group 2: Key Insights from Speakers - Zhao Yihuai, a government official, emphasized the role of the Lingang New Area in financial innovation and the importance of creating a benchmark for financial technology [4]. - Yang Qiang, chairman of the Intelligent Investment Research Technology Alliance, noted the growth of member institutions from 72 to 333 over five years, indicating a shift in AI's role from a tool to an ecosystem [4][5]. - Jia Biao, president of the Shanghai Asset Management Association, highlighted that AI technologies are transforming investment research, advisory, trading, and risk control processes [5]. Group 3: Challenges and Solutions in AI Development - Yang Qiang identified five major challenges in AI model development, including the misalignment of AI expansion laws and hardware limitations, and proposed solutions like federated learning [7]. - Xiao Jing from Ping An Group discussed the three stages of AI development and emphasized the need for high-quality data and robust technical platforms [7]. - Bai Shuo from Hang Seng Electronics focused on how AI is reshaping investment research processes through enhanced efficiency and accuracy [7]. Group 4: AI Applications in Various Industries - The conference featured discussions on AI applications in satellite design, new materials, and semiconductor manufacturing, highlighting investment opportunities in these areas [10][11]. - Chen Bimei from Shanghai Jiangling Technology showcased AI's role in semiconductor manufacturing, emphasizing the market potential due to low domestic production rates [11]. - Future trends in AI include the development of home AI assistants and the modularization of robotics, indicating a shift towards more integrated and user-friendly AI solutions [11]. Group 5: AI in Asset Management - The second roundtable focused on the integration of AI in asset management, with discussions on the need for a reliable data space and the challenges of data governance and talent acquisition [14][15]. - Wu Miao from Ping An introduced "Zhipu Assistant" for knowledge management and collaborative work, aiming to evolve AI from an assistant to a collaborative entity [15]. - The importance of establishing a comprehensive auditing mechanism for AI applications was emphasized to ensure model interpretability and regulatory compliance [15].