Summary of London Stock Exchange Group (LSEG) Update / Briefing Company Overview - **Company**: London Stock Exchange Group (LSEG) - **Date of Briefing**: November 10, 2025 - **Focus**: Innovations, strategic transformation, and financial performance Key Points Industry and Market Position - LSEG operates in a competitive landscape, often ranking in the top three or number one in its markets [3][4] - The company serves non-discretionary functions for clients, indicating essential services [3] - Markets are projected to grow from mid-single digits to double digits, particularly in FTSE Russell and risk intelligence [4] Strategic Initiatives - LSEG has a partnership model based on an open ecosystem, emphasizing collaboration with customers [3] - The company is focused on integrating its various services to enhance customer experience, particularly through its data flywheel [5][6] - LSEG is aligned with long-term industry trends, including the growing demand for trusted data and the digitization of financial services [7][8] Financial Performance - Approximately 75% of LSEG's revenue comes from recurring subscription services, providing stability [9] - The company has achieved a 14% compound growth over the last four years, with earnings per share compounding at 15% over 20 years [10] - LSEG has returned $5 billion via buybacks in three and a half years, representing about 10% of its market capitalization [11] Transformation Journey - The acquisition of Refinitiv was a pivotal moment, transforming LSEG from a regional, equities-focused business to a global player with strong data capabilities [12][13] - The company has exceeded growth expectations post-acquisition, achieving over 6% growth annually [15] - LSEG has successfully realized synergies from the Refinitiv deal, exceeding initial targets for both revenue and cost synergies [16] Engineering and AI Strategy - LSEG is undergoing an engineering transformation to enhance product development and operational efficiency [31][32] - The AI strategy is built on three pillars: trusted data, transformative products, and intelligent enterprise [39][40] - LSEG's data is AI-ready, with 90% of data and feeds revenue based on proprietary data, which is not accessible to public AI models [42] Product Development and Innovation - The company is focused on continuous product innovation, with significant investments in platforms like Workspace and TradeWeb [25][26] - LSEG has launched numerous new products across all divisions in the last year, enhancing its service offerings [27] - The partnership with Microsoft is crucial for co-developing AI-driven products and improving customer experiences [66][69] Customer Engagement and Satisfaction - LSEG emphasizes customer partnerships and satisfaction, with a focus on reducing total cost of ownership for clients [24] - The company is actively working on integrating its data into various platforms, enhancing accessibility and usability for customers [50] Future Outlook - LSEG is committed to ongoing transformation and innovation, adapting to evolving technologies and regulatory changes [28] - The company aims to maintain its competitive edge by leveraging its unique data assets and strategic partnerships [66] Additional Important Insights - The leadership team has undergone significant changes to drive transformation, bringing in industry experts to enhance capabilities [19][20] - LSEG's diversified exposure across asset classes and geographies mitigates risks associated with market fluctuations [10][7] - The company is focused on building a product-led culture, ensuring that customer experience is at the forefront of its operations [38]

点击上方"公众号"可订阅哦！ 兴业证券计算机小组 蒋佳霖/孙乾/杨本鸿/陈鑫/张旭光/杨海盟/桂杨 本周观点聚焦 1、本周 观 点： 财报披露冲刺期将至，底部加仓质优龙头 2、深度跟 踪 ： 信创复盘研究：观往知来，超额成长可期 周观点 财报披露冲刺期将至，底部加仓质优龙头 财报披露冲刺窗口将至，建议底部加仓。 截至到 04/19 ，已正式披露 2024 年年报、 2025 年一季报的计算机公司数量分别为 133 家、 12 家，占比为 40% 、 4% ， 距离财报季结束还有 8 个工作日，财报将密集披露。伴随着财报的披露，以及国际形势的变化，板块近期持续调整，但业绩趋势及产业变化偏正向的格局并未变化。同 时，对于市场关心的月历效应，根据统计，自 2010 年以来，计算机指数在 4/5/6 月上涨概率分别为 20% 、 53% 、 60% 。综上，继续建议底部加仓。 MCP 加速 AI 应用落地，积极关注其进展。 MCP （ Model Context Protocol ）通过标准化接口实现 AI 模型与外部工具 / 数据的无缝交互，有助于 Agent 应用快速搭 建。近期，阿里云百炼、腾讯云、百度地图等均 ...

Core Insights - The Model Context Protocol (MCP) has significantly monopolized the middle layer of Agentic AI, with its usage growing rapidly since its open-source release in November last year [4][5][6] - MCP is likened to a USB-C port, aiming to become a standardized interface for AI applications, facilitating seamless integration and interaction with various data sources and tools [3][21] - The emergence of the MCP ecosystem is evident, with a variety of MCP Clients and Servers, as well as a marketplace and infrastructure developing around it [7][8] Insight 01: MCP's Dominance - MCP has established itself as a dominant middle layer for Agentic AI, allowing systems to provide contextual information to AI models and enabling integration across various scenarios [4][5] - The protocol simplifies the integration process for developers, enhancing the user experience of LLMs by providing a unified way to access data sources [4][5] Insight 02: MCP Ecosystem Development - The MCP ecosystem is rapidly expanding, with a rich variety of MCP Clients and Servers emerging, alongside dedicated marketplaces and infrastructure products [7][8] - MCP Clients can seamlessly connect to any MCP Server to obtain context, while MCP Servers allow tool and API developers to easily gain user adoption [8][9] Insight 03: MCP as a Standardized Interface - MCP serves as a standardized interface between LLMs and data sources, facilitating the transformation of various data types into a unified format for AI applications [21][22] - The protocol redistributes the workload of data transformation, allowing independent developers to create effective connectors for various applications [22] Insight 04: Maximizing Context Layer Effectiveness - To fully leverage AI Agents, three core elements are essential: rich context, a complete tool usage environment, and iterative memory [24] - MCP enhances the effectiveness of the Context Layer by enabling community-driven development and optimization, which is crucial for high-quality AI agents [25] Insight 05: MCP as a Comprehensive Solution - MCP consolidates various existing middle-layer products into a more lightweight and open foundational protocol, impacting competitors like OpenAI's Function Call and LangChain [29][30] - The protocol's modularity and ecological potential are highlighted, allowing for broader adoption and integration across different platforms [31] Insight 06: MCP's Role in Agentic AI - MCP is positioned as an open protocol that facilitates access to context and tools for users who do not have control over the underlying systems [32] - The flexibility of MCP allows it to serve as a robust solution for developers looking to integrate various data sources and tools into their applications [32] Insight 07: Entrepreneurial Opportunities in the MCP Ecosystem - The MCP ecosystem presents three main entrepreneurial opportunities: Agent OS, MCP Infrastructure, and MCP Marketplace [33][35] - The development of scalable MCP Servers and a marketplace for discovering and installing MCP Servers are key areas for growth and innovation [39][40]