转自：新华财经 2025年10月18日下午，"2025指数与量化投资发展论坛"在上海市虹口区北外滩来福士圆满举行。作为"全球资产管理中心 上海国际活动周2025"专场之 一，本次论坛由上海资产管理协会（简称"协会"）主办，虹口区金融服务中心、上海资产管理协会指数与量化投资专业委员会承办，上海对冲基金园区 支持。 论坛以"证券投资的科技思想、方法原理"为主题，汇聚了政府部门、金融监管部门、证券交易所，以及资管及财富管理、数智技术、学术研究、专业服 务机构约200位专业技术人员参与。 10月18日，上海对冲基金园区1期正式迁址北外滩来福士。（主办方供图） 贾飙和协会副会长、指数与量化投资专业委员会主任委员陶耿为协会指数与量化投资专业委员会正式揭牌。协会副会长、中行（上海）交易总部党委 委员、副总裁黄德主持论坛。 贾飙在致辞中表示，指数与量化投资快速发展，正从产品结构、技术手段、竞争格局等多个维度，对资产管理行业生态产生重要影响。此次新专委会 成立标志着协会专业体系建设迈出关键一步。未来，协会将努力承担好强化上海全球资管中心城市功能建设职责，推动资产管理业发展与区域发展深 度融合。 陶耿在致辞中表示，指数与量化投资 ...

一天之内，接连两场高规格会见，对象都是在美国商界举足轻重、在中国也家喻户晓的人物，这绝不是巧 合。当前，特朗普政府刚刚挥起新一轮关税大棒，中方也出台了稀土管制等反制措施，贸易战大有升级失 控的风险。全世界都在担心，这两个经济巨头的碰撞会殃及池鱼。 就在中美贸易战火气腾腾、两边互相加码关税的节骨眼上，美国外交圈的一位重量级人物——坎贝尔，突 然站出来说了句大实话：特朗普心里其实"非常想去中国"。他预测，明年年初，特朗普很可能就会访问北 京。 这个声音之所以引人注目，是因为它点破了当前中美关系一个心照不宣的驱动力：双方表面上吵得凶，但 台面下都在忙着为未来几个月的高层会谈，尤其是可能的元首会晤搭桥铺路。坎贝尔直言，现在美国财政 部（而不是传统上的白宫或国务院）成了对华外交的主角，这说明特朗普政府更倾向于从"做生意"的角度来 看待中美关系。 微妙时机的高规格会见 就在坎贝尔发声的几乎同一时间，北京也做出了动作。10月16日，中共中央政治局委员、中央外办主任王 毅在北京会见了美国金融巨鳄——黑石集团的董事长苏世民。无独有偶，中国商务部部长王文涛也在同一 天会见了苹果公司的CEO库克。 总而言之，坎贝尔的直言和王毅的会 ...

关于大模型对金融机构的赋能与重塑，浦银理财金融科技部总经理吴苗作为银行理财子公司代表，在活 动中分享了浦银理财在大模型应用方面的探索与实践。 21世纪经济报道记者 余纪昕 上海报道 今年8月，国务院印发《关于深入实施"人工智能+"行动的意见》，从国家层面对各行业人工智能应用提 出指导，并明确了时间表与路线图。在此背景下，上海作为全球金融中心，正积极推动金融科技与人工 智能的深度融合。早在2024年9月，上海市政府办公厅就发布了《上海高质量推进全球金融科技中心建 设行动方案》，提出力争用3至5年将上海建成具有全球引领性的金融科技中心。 10月17日上午，"临港新片区金融科技集聚区发展座谈会——暨智能投研技术联盟（ITL）成员机构代表 座谈会"在临港中心举办。来自上海市委金融办、临港新片区管委会、金融机构、金融科技企业及投资 机构等代表齐聚一堂，共商金融科技高质量发展路径。 同天下午，第六届1024资管科技开发者大会（ITDC 2025）也在临港中心顺利举行。大会由临港新片区 管委会指导，资产管理协会、智能投研技术联盟等共同主办。 从产业AI到金融AI，上海资产管理协会党的工作小组组长、秘书长韩康在本次座谈中指出 ...

Group 1 - The 2025 Index and Quantitative Investment Development Forum was held in Shanghai, focusing on "the technological ideas and methodological principles of securities investment" [1] - Approximately 200 professionals from various sectors including government, financial regulatory bodies, and asset management participated in the forum [1] Group 2 - The Shanghai Hedge Fund Park's first phase relocation ceremony took place during the forum, moving from Citic Square to North Bund Raffles [2] - The park, established in 2013, is the first specialized financial institution cluster focused on the hedge fund industry in China, contributing to Shanghai's international financial center development [2] - Nearly 50 quantitative private equity firms have settled in the park, making it a hub for quantitative hedge funds in the country [2] Group 3 - The Shanghai Asset Management Association's Index and Quantitative Investment Professional Committee was officially established, focusing on key areas such as professional technology application, compliance, risk management, and industry development [2]

Group 1: India as a Growth Market - India is recognized as the fastest growing large economy, prompting global corporations to formulate specific plans for the market [1] - The Indian market is largely untapped, characterized as a country of savers rather than investors, presenting opportunities for local investing and retirement products [2] - The beverage alcohol market in India is experiencing rising social acceptance, particularly among younger consumers, contributing to its status as the second-largest whiskey market globally, growing at approximately 8% annually [3] Group 2: Company Perspectives - BlackRock's JioBlackRock venture aims to enhance access to local investment opportunities in India [2] - Brown-Forman views India as a significant long-term growth engine for its Jack Daniel's brand, driven by premiumization and changing social attitudes towards alcohol [2] - Firan Technology Group is establishing a manufacturing facility in Hyderabad, citing India's cost advantages and supportive policies as key factors [3] - Pernod Ricard reported steady growth in India despite challenges from a sharp excise tax increase in Maharashtra, which raised prices and impacted sales [4][5]

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 Insights - The conference focused on the integration of artificial intelligence (AI) in asset management, highlighting the historical opportunities presented by "AI + Asset Management" [2][3] - The event gathered over 300 participants from various sectors, including government, finance, and technology, to discuss the application and development trends of AI in asset management [1] Group 1: AI Integration in Asset Management - The Shanghai Lingang New Area aims to become a benchmark financial technology hub by leveraging its unique advantages in financial openness and innovation [2] - The Intelligent Investment Research Technology Alliance (ITL) has grown from 72 to 333 member institutions over five years, indicating a significant expansion in the sector [2] - AI technologies, particularly large models, are transforming asset management processes such as research, advisory, trading, and risk control [3] Group 2: Challenges and Solutions in AI Development - Key challenges in AI model development include the misalignment of AI expansion laws with hardware capabilities, error accumulation in multi-agent collaboration, and ensuring AI safety and value alignment [4] - Solutions proposed include the "Federated Teacher-Student Model" for collaborative learning between general and specialized models [4] - The need for high-quality vertical data, foundational technology platforms, and enhanced AI safety and ethical governance was emphasized as critical for AI's successful implementation in finance [4] Group 3: Practical Applications of AI - AI is being applied in various industries, including satellite design, new materials development, and semiconductor manufacturing, presenting investment opportunities [6][7] - The integration of AI in semiconductor manufacturing is particularly highlighted, with a focus on improving operational efficiency and increasing domestic production rates [6][7] - The financial sector is seeing a shift from "information disparity" to "model disparity," necessitating improvements in data governance and organizational structures to fully leverage AI capabilities [8] Group 4: Future Directions and Ecosystem Development - The establishment of a trusted data space in the securities industry aims to reduce the complexity and cost of applying large models, fostering AI innovation [8] - The importance of a multi-disciplinary team with a blend of curiosity and cross-functional skills was identified as essential for overcoming challenges in AI integration [8][9] - The Shanghai Asset Management Association is focusing on enhancing the global competitiveness of Shanghai as an asset management center through digitalization and AI integration [10]

大模型挑战与金融AI实践 在主题演讲环节，杨强院士深入剖析了人工智能大模型发展面临的五大挑战，包括AI扩展定律与硬件 摩尔定律的不协调、多智能体协同中的错误叠加、人工智能安全与价值观对齐等，并提出了"联邦师生 大模型"的解决方案，通过联邦学习实现通用大模型与垂直小模型的协同学习。 2025年10月17日，第六届1024资管科技开发者大会（ITDC 2025）在上海临港中心（世界顶尖科学家论 坛永久会场）滴水厅成功举办。大会由中国（上海）自由贸易试验区临港新片区管理委员会指导，由上 海资产管理协会、智能投研技术联盟（ITL）、上海临港新片区商会、上海临港新片区投资控股（集 团）有限公司主办。 本次大会是上海资产管理协会主办的"全球资产管理中心上海国际活动周2025"系列重要活动之一，同时 也是智能投研技术联盟（ITL）成员机构发展年会。本次大会以"人工智能+，从产业AI到金融AI"为主 题，汇聚来自政府部门、金融基础设施、银行、保险、公募基金、券商资管、金融科技企业等领域的 300余位嘉宾，共同探讨人工智能技术在资产管理行业的前沿应用与发展趋势。 "AI+资管"正迎来历史性机遇 在大会开幕仪式上，中国（上海）自 ...

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].

Core Insights - The discussion at the Global Wealth Management Forum highlighted how artificial intelligence (AI) is fundamentally reshaping the future of asset management [2] - AI is seen as a tool to enhance productivity in service-oriented companies, narrowing the gap with manufacturing firms [2] - Historical examples show that aging economies have successfully adopted new technologies to improve productivity, with AI being the next wave of innovation [2] Group 1 - AI has reached a level of maturity that allows service companies to leverage it for significant productivity gains [2] - The most exciting and disruptive applications of AI are emerging directly in investment management, with AI systems approaching the skill level of human investment managers [2] - The challenge to human judgment in asset management is not about "if" AI will compete, but "when" it will match or exceed traditional human advantages [3] Group 2 - Future exploration is needed on how to apply AI in areas such as asset allocation, private assets, external fund selection, and money market funds [3] - Many projects utilizing AI in asset management are expected to yield results soon [3]