Core Insights - The true value of AI in the financial sector fundamentally depends on the data utilized [1] - The course aims to optimize various financial datasets for AI applications, including macroeconomic indicators, pricing and reference data, company data, risk intelligence, and unstructured text [1][2] Course Content - Participants will learn how to extract maximum value from financial data, empowering their AI strategies whether building predictive models, trading algorithms, or risk analysis applications [2] - The course will cover common data pitfalls, best practices in feature engineering, and the technical and data requirements necessary for scalable AI systems [4] Event Details - The event is scheduled for November 28, 2025, from 15:30 to 16:30 [3] Overview of Financial Data and AI Applications - The course will provide a comprehensive overview of financial AI, the realities behind the hype, practical optimization of financial data for AI, and detailed requirements for AI data and technology [5]

Core Insights - The true value of AI in the financial sector fundamentally depends on the data utilized [1] - The course aims to optimize various financial datasets for AI applications, including macroeconomic indicators, pricing and reference data, company data, risk intelligence, and unstructured text [1][2] Course Content - Participants will learn how to extract maximum value from financial data, empowering their AI strategies whether building predictive models, trading algorithms, or risk analysis applications [2] - The course will cover common data pitfalls, best practices in feature engineering, and the technical and data requirements necessary for scalable AI systems [4] Event Details - The event is scheduled for November 28, 2025, from 15:30 to 16:30 [3] Financial Data Overview - The course will provide a comprehensive overview of financial data and AI applications, addressing the realities behind the hype of financial AI, practical optimization of financial data for AI, and detailed requirements for AI data and technology [5]

Core Insights - The true value of AI in the financial sector fundamentally depends on the data utilized [1] - The course aims to optimize various financial datasets for AI applications, including macroeconomic indicators, pricing and reference data, company data, risk intelligence, and unstructured text [1][2] Course Content - Participants will learn how to extract maximum value from financial data, empowering their AI strategies whether building predictive models, trading algorithms, or risk analysis applications [2] - The course will cover common data pitfalls, best practices in feature engineering, and the technical and data requirements necessary for scalable AI systems [4] Event Details - The event is scheduled for November 28, 2025, from 15:30 to 16:30 [3] Financial Data Overview - The course will provide a comprehensive overview of financial data and AI applications, addressing the realities behind the hype of financial AI, practical optimization of financial data for AI, and detailed requirements for AI data and technology [5]

Hello, It Is Me.&& we are backLIVE RIGHT NOW --> https://t.co/QStcdJBhD4- Crypto Price action- Financial Data FINALLY coming out- How Im somehow pulled off profitable meme traders&& soooo much more🔋by @yeet https://t.co/oGFQ6BF72X ...

Core Viewpoint - The strategic partnership between Guotai Haitong and Bloomberg aims to enhance collaboration in financial data, fintech, and market expansion, leveraging Bloomberg's global resources to support Guotai Haitong's international business growth and brand development [2][4]. Group 1: Partnership Details - The signing ceremony was attended by key figures including Guotai Haitong's Chairman Zhu Jian and Bloomberg's founder Michael Bloomberg [2]. - The partnership will focus on improving Guotai Haitong's capabilities in international business, global data governance, wealth management, and overseas family office services [4]. Group 2: Company Backgrounds - Bloomberg, founded in 1981 and headquartered in New York City, operates 167 branches globally and employs over 26,000 people, providing trusted financial data and insights [4]. - Guotai Haitong was established through the merger of Guotai Junan and Haitong Securities in 2025, with a presence across 31 provinces and regions in China, and services extending to 17 countries and regions [4].

Core Insights - The current innovation in financial technology has shifted risks from traditional credit and market risks to technology, model, and compliance risks [4][5] - Digital technology has fundamentally transformed social and financial relationships, creating new legal challenges and opportunities for law [3][4] - The definition of financial data is crucial, as it affects regulatory and market data classification, which can lead to confusion in subsequent rules [4] Group 1: Financial Technology Risks - Financial technology innovation has transitioned risks from traditional credit and market risks to technology, model, and compliance risks [4] - New challenges in the financial industry include issues such as distorted information from IoT devices affecting insurance claims and liability for erroneous advice from AI financial advisors [4] Group 2: Legal and Regulatory Framework - The future legal and regulatory framework should develop capabilities to penetrate technological black boxes and define responsibilities in decentralized environments [5] - There is a need to enhance the protection of financial consumer rights within the evolving legal landscape [5] Group 3: Digital Technology and Law Interaction - Digital technology has created new legal issues, such as data ownership disputes, which require legal responses [3] - Law must not only regulate technology but also serve as a tool to promote healthy technological development, indicating a bidirectional relationship [3]

Market Trends - Rising retail and institutional demand for crypto assets is observed [1] - The need for more accessible analytics in the crypto space is highlighted [1] Regulatory Landscape - Regulatory clarity is crucial for the development of the crypto market [1] Data & Trust - Trust in data is essential for the growth and adoption of crypto assets [1]

Regulatory Focus - The Trump Administration seeks comments on a controversial rule governing access to consumer financial data [1] Data Access - The rule concerns access to consumer financial data [1]

Blockworks has great DAT dataBlockworks Research (@blockworksres):UPDATE: We now have a Financial Data tab for our Treasury Company Dashboard that tracks metrics from public 10-Q and 10-K filings.We currently track the sources of funding for Treasury Companies and key metrics on each company's capital structure. https://t.co/uNknSaFeMo ...

Consumer Software Industry Concerns - Consumer software CEOs are worried about agents taking over, leading to a paradigm shift away from web pages [1] - The rise of agents could make the traditional browser obsolete, reducing it to a glorified markup reader [6] Perplexity's Strategic Opportunities - Perplexity has the potential to replace Bloomberg in the financial information and data sector, a market worth $100 billion [4][5] - Focusing on a specific vertical and owning it is a good strategy for Perplexity, especially with unique data sources [10] Apple's Potential Acquisition of Perplexity - There is speculation that Apple might acquire or partner with Perplexity for distribution, given the Justice Department case against Google [9] - However, acquiring Perplexity may not solve Apple's AI challenges, as Apple has missed previous AI opportunities [10] Bloomberg's Weaknesses - Bloomberg's terminal is considered atrocious, terrible, and limited, presenting an opportunity for a better product [4] - Bloomberg's core usability and UI have not evolved, despite its messaging platform being valuable for trading [14]