人民财讯10月10日电，据深圳前海股权交易中心(以下简称"深圳股交")官方消息，近日，深圳股交已与 企查查科技股份有限公司(以下简称"企查查")签署数据服务协议，正式建立合作关系。基于此次合作， 企业在深圳股交完成股份托管登记以及后续股份变动后，若同意授权使用数据，企查查将在其平台同步 公示深圳股交企业股东名册数据。除股东名册同步公示外，针对在深圳股交办理有限合伙企业财产份额 质押业务的企业，企查查亦将对其添加标签或同步进行公示。除深圳股交外，近期企查查已与北京股权 交易中心、上海股权交易中心、江苏股权交易中心、广东股权交易中心、海南股权交易中心、青岛蓝海 股权交易中心等全国多个股权交易市场达成相关合作。 ...

格隆汇10月7日｜据彭博，标普全球将推出创新的加密生态系统指数，这是一种将加密货币与加密货币 相关股票相结合的全新方式。 ...

Core Insights - The ADP report unexpectedly became the focus of the market due to the absence of official employment data from the U.S. government, which is currently in a shutdown [1] - The report indicated a surprising decrease of 32,000 jobs in the private sector for September, significantly below market expectations, leading to initial declines in U.S. stock index futures and a drop in the 10-year U.S. Treasury yield [1][3] - The report raised more questions than answers regarding the U.S. economic situation, complicating investors' assessments [3] Group 1: ADP Report Analysis - The ADP report has historically been inconsistent in predicting official non-farm employment data, and this month's report was particularly unusual [4] - A technical recalibration based on the Quarterly Census of Employment and Wages (QCEW) led to a reduction of 43,000 jobs in the ADP report, indicating that market interpretations of weak data should be approached with caution [4] - The reliance of private data on official statistics highlights the challenges in data collection and the limitations of private data providers [5] Group 2: Challenges in Data Collection - Data collection is a labor-intensive and costly process, and private data providers are not yet equipped to independently guide the market [5] - Although alternative data from private suppliers is becoming valuable, it is often only accessible to institutional investors and lacks uniform quality [5] - The public sector's role in data collection is crucial, especially for specific demographic employment data, which private entities may not prioritize due to a lack of profit motivation [5] Group 3: Issues with Official Statistics - The Bureau of Labor Statistics (BLS) faces challenges, including budget cuts and resource constraints, which have raised concerns about data quality [6] - The BLS has been criticized for unevenly disclosing data to a select group of "super users," further undermining confidence in its statistics [6] - Political pressures have also affected the BLS, as seen in the previous administration's actions that questioned the integrity of the agency [6]

Core Viewpoint - Financial services institutions are increasingly recognizing the diverse application value of migrating pricing and reference data to the cloud, which includes modeling, process automation, and AI-driven innovation projects [2][4]. Group 1: Cloud Migration Benefits - The DataScope Warehouse enables enterprises to quickly and conveniently access necessary pricing and reference data in the cloud, enhancing efficiency across the trading lifecycle [4][5]. - A recent global survey by LSEG revealed that 47% of respondents are already using market and pricing data in the cloud, while 38% are utilizing cloud-based reference data, indicating that cloud data is becoming a core driver of fintech transformation and business agility [2][4]. Group 2: DataScope Warehouse Features - DataScope Warehouse was officially launched in September 2024, allowing enterprises to access LSEG's complete pricing and reference data globally, with new customers able to connect within 24 hours, significantly speeding up deployment compared to traditional on-premises solutions [5][6]. - The platform is continuously optimized, with new features, cloud distribution interfaces, and additional datasets set to be released over the next 18 months [4][8]. Group 3: Cost Efficiency and Management - DataScope Warehouse significantly reduces total ownership costs by providing a solution that allows enterprises to efficiently maintain and manage their data needs [6][7]. - The service is natively deployed on Snowflake and Google Big Query platforms, facilitating rapid and secure data sharing across various jurisdictions, thus enhancing global operations and data management efficiency [7]. Group 4: Future Developments - Upcoming features for DataScope Warehouse include "Change Tracking," which will help enterprises manage data deployment and governance more effectively by notifying users of data changes [8]. - Additional content, including corporate actions data, will be introduced in the coming months to support financial institutions' evolving business needs [9].

Key Takeaways FactSet will release 4Q25 results on Sept. 18, before market open.Revenues are expected to be $592.6M, suggesting a 5.6% y/y rise, led by growth across regions.EPS is projected at $4.15, indicating an 11% increase from the prior-year quarter's actual.FactSet Research Systems Inc. (FDS) is set to report fourth-quarter fiscal 2025 results on Sept. 18, before market open.FDS surpassed the Zacks Consensus Estimate in the trailing four quarters, delivering an average surprise of 1.7%.FactSet’s Q4 E ...

人工智能正在进入"智能体群"时代，并推动各行业"技术平权"。煜马（深圳）数据信息有限公司（简 称"煜马数据"）近日正式发布其自主研发的"AgentBull金融智能体"。 目前，人工智能在金融领域的应用正进入深水区，依赖单一超大型大语言模型的人工智能技术路径，在 金融领域这一对精准度、时效性和成本效益要求极高的场景下，逐渐暴露出难以调和的内在矛盾。针对 这一行业共同面临的挑战，AgentBull提出了"多智能体交互框架"的破局路径。 在业内人士看来，数量庞大的智能体之间彼此交互、执行任务、交换数据、交换信息，甚至交换任务， 而人类与这些智能体群的交互，将构成所谓的"智能体经济"。而Agent（智能体）将重塑企业流程，"超 级个体+agent"会带来巨大的结构性变革。 煜马数据介绍，该框架并非试图打造一个无所不晓的"通才"，而是构建一个由各领域顶尖"专家"组成的 协同作战团队。AgentBull将复杂的投研任务拆解，由数据抓取、产业链逻辑、量化分析与风险预警等 多个专精智能体协作完成。 该产品标志着金融AI正从"秘书级"信息归纳迈向"专家级"决策辅助，也意味着金融投研的"技术平权"正 在逐步落地。 ...

Core Viewpoint - Artificial Intelligence (AI) is rapidly transforming the financial services landscape, with a strong emphasis on the importance of data quality for the success of AI models [3][4][62]. Group 1: Importance of Data in AI - The performance of AI models is entirely dependent on the quality of the data they absorb, as highlighted by LSEG's CEO David Schwimmer [3]. - Financial data is complex, fragmented, and often subject to regulatory constraints, encompassing both structured and unstructured formats [3][4]. - Optimizing financial data for AI requires domain expertise, robust infrastructure, and meticulous governance [3][4]. Group 2: Challenges in Financial AI - Up to 85% of financial AI projects fail due to data quality issues, talent shortages, and strategic misalignment [4]. - Gartner predicts that 30% of generative AI projects will be abandoned after the proof-of-concept phase due to poor data quality [4]. Group 3: Data Categories and Optimization Techniques - **Macroeconomic Data**: Includes indicators like CPI, GDP, and unemployment rates, crucial for predictive models and trading signals [9]. - Optimization techniques involve using point-in-time (PIT) and real-time data to avoid biases from historical corrections [11]. - **Pricing Data**: Forms the basis for security valuation, including real-time quotes and historical prices [14]. - Risks include misleading models due to lagged and revised data [15]. - **Reference Data**: Provides descriptive details about securities and entities, essential for filtering trading eligibility and detecting anomalies [20]. - Optimization techniques include creating master mapping tables and tracking data lineage [24]. - **Symbol Mapping**: Involves using identifiers like ISIN and CUSIP to map and stitch datasets together [27]. - Risks include identifier changes due to corporate actions [29]. - **Unstructured Text**: Comprises news, research reports, and records, rich in insights but challenging to process [35]. - Techniques include using natural language processing for summarization and sentiment analysis [38]. - **Company Data**: Includes structured financial data and unstructured disclosures, vital for valuation and ESG analysis [42]. - Risks involve misinformation and misinterpretation [43]. - **Risk Intelligence Data**: Encompasses sanctions, politically exposed persons, and negative news, critical for compliance and fraud detection [49]. - Optimization techniques focus on standardizing names and addresses [51]. - **Analysis**: Used for valuation, hedging, and risk metrics, potentially involving local or cloud-based computing engines [57]. - Techniques include automating anti-money laundering and fraud detection [59]. Group 4: Conclusion on AI Readiness - The success of AI in financial institutions hinges not only on sophisticated algorithms but also on the integrity and readiness of the underlying data [62]. - Optimizing financial data is an ongoing task requiring collaboration among data engineers, domain experts, and AI practitioners [62].

Core Viewpoint - Bloomberg has launched a new green bond data column to help insurance companies and financial institutions comply with sustainable investment regulations set by the Hong Kong Insurance Authority (HKIA) [1] Group 1: New Data Column - The new "HKIA_Sustainable_Indicator" data column provides clear and independent classification for green bonds that meet HKIA's latest regulatory guidelines [1] - The HKIA's regulatory guidelines utilize a Risk-based Capital regime (RBC) to assess the compliance of green assets [1] - The data column is now available on Bloomberg terminals and Bloomberg Enterprise Data Services, supporting portfolio construction, regulatory reporting, and risk analysis [1] Group 2: Market Need and Commitment - Joshua Kendall, Bloomberg's Head of Sustainable Fixed Income Products, emphasized the need for transparent and reliable financial tools as Hong Kong solidifies its position as a green finance hub in Asia [1] - The new HKIA data column assists insurance companies in integrating the latest valuation and capital guidelines into their workflows [1] - This initiative reflects Bloomberg's commitment to providing sustainable finance and regulatory solutions to help clients make informed decisions and comply with regulations [1] Group 3: Data Support and Features - The data column is supported by Bloomberg's extensive sustainable bond database, which includes detailed information on bond use of proceeds, third-party certifications, issuer disclosures, and classification correspondences [1] - Users can filter and monitor green bonds that comply with HKIA and other global standards based on publicly disclosed data [1] - This column serves as a complement to Bloomberg's broader fixed income product offerings [1]

Core Viewpoint - The launch of Huawei Mate XTs "Extraordinary Master" folding phone signifies a strategic collaboration with three leading companies: Wind, Tencent, and Feishu, highlighting the development of a comprehensive smart interconnectivity ecosystem based on HarmonyOS [1][3]. Group 1: Strategic Partnerships - The inclusion of Tencent represents immersive experiences in entertainment and social interaction [3]. - Feishu symbolizes new productivity in enterprise office and collaboration [3]. - Wind opens up possibilities for professional-grade industry applications, covering the entire user journey in work, life, and investment [3]. Group 2: Wind's Role - Wind's participation is particularly notable as it compresses a complete PC-level financial terminal into a mobile device, addressing the complex needs of financial professionals [4][10]. - The Mate XTs features a 10.2-inch display that allows users to access vast amounts of global data seamlessly [6]. - Multi-tasking capabilities enable users to view financial reports on one screen while accessing databases and market sentiment on another [7]. Group 3: AI Integration - Wind is revolutionizing the work of financial professionals with AI Agents that provide 24/7 assistance [11]. - The AI Briefing Agent delivers global market updates and stock alerts every morning [11]. - The Investment Morning Report Agent monitors global markets overnight and presents results at dawn [11]. Group 4: Future Vision - The collaboration of these three companies at the launch not only supports the Mate XTs but also points towards a future of cross-industry and cross-scenario smart interconnectivity [12]. - For Huawei, this initiative represents both a product and a strategic direction, while for Wind, it is an opportunity to showcase its capabilities [12]. - Users can expect smart devices to evolve from mere tools to integral ecological partners in their daily lives [12]. Group 5: Industry Impact - The Mate XTs folding phone is perceived as a new possibility for financial professionals, investors, and office workers [13]. - Wind's technology allows financial tasks to be performed on-the-go, effectively putting a PC in the user's pocket [13]. - Tencent enhances the immersive experience for entertainment, while Feishu facilitates efficient collaboration across different scenarios [13].

Core Viewpoint - Huawei has invited three strategic partners: Wind, Tencent, and Feishu, representing finance, entertainment, and collaboration, respectively, signaling the construction of a comprehensive smart interconnection ecosystem with the HarmonyOS [1][4]. Group 1: Why These Three Companies? - Tencent's involvement signifies an immersive experience in entertainment and social interaction [4]. - Feishu represents new productivity in enterprise office and collaboration [4]. - Wind opens up possibilities for professional-grade industry applications [4]. Group 2: Wind's Role - Wind's introduction is notable as it compresses a complete PC-level financial terminal into a smartphone, utilizing Huawei's Mate XTs foldable screen [8][10]. - The device features a 10.2-inch large screen for browsing vast data and multi-tasking capabilities, allowing simultaneous viewing of financial reports and databases [9]. - Instant generation of reports and data extraction is facilitated by the Alice series functions, enabling a seamless workflow for finance professionals [10]. Group 3: The Agent Revolution - Wind is transforming the work style of finance professionals with AI Agents that provide 24/7 assistance [11]. - Various agents are designed to deliver market updates, generate reports, and analyze performance, acting as personal research assistants [12]. - The future is envisioned as an era dominated by these agents, enhancing productivity and efficiency for finance professionals [12]. Group 4: Cross-Industry Innovation - Huawei's choice of these three companies reflects their leadership in finance, entertainment, and office collaboration, supporting the application of the foldable screen [14]. - The collaboration indicates a strategic move towards creating a cross-industry, all-scenario smart interconnection ecosystem [14]. - For users, this signifies a shift from tools to smart ecological partners in their daily lives [14]. Group 5: Future Implications - The Mate XTs foldable screen is positioned as a new possibility for finance, entertainment, and office collaboration sectors [15]. - Wind enables finance professionals to carry a PC in their pocket, while Tencent enhances immersive experiences, and Feishu facilitates efficient collaboration [15]. - The foldable device and its capabilities represent a significant advancement in how professionals interact with technology [15].