Core Viewpoint - The article discusses the decline of the UK steel industry, focusing on the conflict surrounding the Scunthorpe steel plant, which is now owned by a Chinese private enterprise, and the implications of this ownership on the UK's ability to produce primary steel. Group 1: Historical Context - Scunthorpe was once a significant steel production hub in England, known for its clean factories and green spaces, earning the nickname "Industrial Garden" [5] - By the 1960s, Scunthorpe was one of the top five steel production bases in the UK, but the industry has since faced a dramatic decline [5][9] - The UK steel industry produced 2.8 million tons of steel in 1970, but by 2023, this figure had plummeted to 5.6 million tons, marking a decline of 3.68% annually [24] Group 2: Current Industry Status - As of 2023, the UK steel industry employs approximately 37,000 workers and produces 5.6 million tons of steel, with 76% being primary steel and 24% recycled steel [9] - The Scunthorpe plant and the Tata Steel plant in Port Talbot are the last remaining facilities producing primary steel in the UK, and both are facing significant operational challenges [13][15] Group 3: Ownership and Economic Implications - The Scunthorpe plant is owned by China's Jingye Group, which acquired it after a series of ownership changes, including Tata Steel and Greybull Capital [45][39] - The UK steel industry is now largely controlled by foreign entities, with Tata and Jingye employing about 11,200 workers, representing one-third of the total workforce in the sector [15][18] - The closure of primary steel production facilities would leave the UK as the only G7 country unable to produce its own primary steel, raising concerns about national security and industrial capability [13][15] Group 4: Environmental and Political Challenges - The UK government has been caught in a dilemma between supporting environmental initiatives and maintaining domestic steel production capabilities [31][67] - The proposed transition from blast furnaces to electric arc furnaces by Jingye is seen as a cost-saving measure but threatens to eliminate 1,500 to 2,000 jobs [55][57] - The UK Parliament has intervened to prevent the closure of the Scunthorpe plant, reflecting the tension between environmental goals and industrial sovereignty [58][60]

问题在于，联储不是早就放话，说要缩表？说要让市场回归自主运作？可眼下的动作怎么看都像是在暗中兜底。这种兜底，看似缓解了债市 抛压，实则埋下了更大的隐患。你不能一边说自己退出，一边在暗地里伸手。市场不是那么好骗的。 更关键的是，这种操作一旦持续下去，会给市场释放出危险信号。投资者会以为，美债风险上升了，美联储要保底。这种心理预期，一旦蔓 延，美元会先感冒。看看近两周美元指数的表现，震荡幅度在加剧，而不是收敛。美股、美债、美元三者之间，本来就是一个危险的平衡。 美联储一旦在其中一角动了手，其他两个就可能出问题。 我们总说不可能三角，这并非空谈。美联储要压通胀，又想稳汇率，同时维持债务市场稳定，这三个目标，任何一个的实现都需要牺牲另一 个。如今它在暗中撑债市，是不是意味着已经默认牺牲美元了？如果是，那资本市场该怎么走，会不会又是一场类似2008年的剧本？ 看看资金的走向，5月以来，大量海外机构资金流出美债市场，亚洲、欧洲的主权基金开始显著减持。与此美国本土的银行系统也在边缘徘 徊，根本没能力大举接盘。而美联储此时动用工具缓解流动性，你说是巧合，谁信？ 美联储到底在打什么算盘？有人说，它又一次悄悄出手了。这回，不是升 ...

编者按： 十载春华秋实，鉴往知来；十年星河璀璨，与光同行。自破茧初啼至引领风潮，"金长江"评选始终以专业为炬、以公正为尺，丈量中国私募基金行业的奔腾浪 潮。值此华章再启之际，证券时报·券商中国倾情推出"金长江风华录·十年十人"，特邀十位穿越牛熊周期的行业翘楚，以躬身力行的灼见为经纬，以栉风沐雨的 征程为注脚，共同镌刻一部激荡人心的奋进诗篇。此间星霜，既见群峰竞秀，亦显大江奔流。 本期是"寻访金长江之十年十人"第二期。券商中国记者走进百亿量化私募茂源量化，茂源量化创始人郭学文接受了记者的专访。 他14岁考入清华，博士后从事气候变化大模型研究，还曾先后创立两家科技企业，均被上市公司收购，其个人经历相当丰富和传奇。2013年，郭学文创办茂源量 化，编写了国内最早的高频交易策略，2018年发行第一只股票产品，2020年启动资管业务，2021年突破百亿规模。 在茂源量化的办公室，挂着一幅"量化投资之父"詹姆斯·西蒙斯与丘成桐教授讨论数学问题的手稿，时间是2020年9月14日。郭学文告诉记者，这份手稿是由丘成桐 教授赠送，当时已经82岁高龄的西蒙斯，在听丘先生讲座时与其讨论数学问题，依然认真地手写下了密密麻麻的问题，这种 ...

Core Insights - The rise of AI large models, particularly with the advent of ChatGPT, has sparked discussions about the potential of general artificial intelligence leading to a fourth industrial revolution, especially in the financial sector [1][2] - The narrative suggesting that the Western system, led by the US, will create a technological gap over China through its "algorithm + data + computing power" advantages is being challenged as more people recognize the potential and limitations of AI [1][2] Group 1: Historical Context and Development - The concept of artificial intelligence dates back to 1950 with Alan Turing's "Turing Test," establishing a theoretical foundation for AI [2] - The widespread public engagement with AI is marked by the release of ChatGPT in November 2022, indicating a significant shift in AI's development trajectory [2] Group 2: Current State of AI in Industry - The arrival of large models signifies a new phase in AI development, where traditional machine learning and deep learning techniques can work in tandem to empower manufacturing [4] - AI applications in the industrial sector are transitioning from isolated breakthroughs to system integration, aiming for deeper integration with various industrial systems [5] Group 3: AI's Impact on Manufacturing - AI can enhance productivity, efficiency, and resource allocation in the industrial sector, serving as a crucial engine for economic development [5] - The current landscape in China features a coexistence of large and small models, with small models primarily handling structured data and precise predictions, while large models excel in processing complex unstructured data [5][6] Group 4: Challenges in AI Implementation - AI's application in manufacturing is still in its early stages, with significant reliance on smaller models for specific tasks, while large models are yet to be fully integrated into production processes [8][9] - The industrial sector faces challenges such as high fragmentation of data, lack of standardized solutions, and the need for highly customized AI applications, which complicates the deployment of AI technologies [10][11] Group 5: Future Directions and Strategies - The goal is to achieve a collaborative system of large and small models, avoiding a singular focus on either, to explore the boundaries of AI capabilities and steadily advance application deployment [20][21] - A phased approach is recommended for AI integration in industry, starting with traditional small models in high-precision environments and gradually introducing large models in less critical applications [19][24] - The development of a robust evaluation system tailored to industrial applications is essential for assessing the performance of AI models in real-world settings [19][26]

想不到一向"浓眉大眼"的迈瑞，也"暴雷"了。 4月28日晚间，在迈瑞医疗公布年报和一季报后，股吧和雪球的讨论区便瞬间炸开了锅。 迈瑞医疗2024年营业收入367.3亿元，同比增长5.1%；归母净利润116.7亿元，同比增长0.7%，剔除 财务费用影响后的增速为4.4%；经营性现金流净额124.3亿元，同比增长12.4%。 2025年第一季度，公司实现收入 82.37 亿元，同比-12.12%；实现归母净利润 26.29 亿元，同 比-16.81%。 对于常年保持20%增长的"好学生"迈瑞来说，这张连续两个季度业绩miss的成绩单，还是有些过于"惊 喜"了。 但出乎大家意料的是，4月29日开盘之后，迈瑞医疗在小幅低开以后迅速冲高，收盘微涨。考虑到最近 A股对于业绩不达预期这事比较敏感，更显得难能可贵。 由此可见在一季报中"前低后高，逐季改善"的业绩指引，还是赢得了机构投资者的信任票。 而除了实实在在的业绩数字之外，迈瑞医疗的数智化转型，在更高的维度上决定了企业的未来。 从"数据孤岛"到"智能生态" 从ChatGPT到DeepSeek，从IBM Watson到AlphaFold3。没有人可以否认，全球医疗行业正在 ...

Core Viewpoint - The article discusses the implications of recent market movements influenced by U.S. President Trump's decisions, highlighting potential insider trading and the need for strategic asset allocation in volatile markets [1][2][5][6]. Group 1: Market Reactions and Insider Trading - President Trump's tweet led to a significant surge in the Nasdaq index by 12.16%, with the total market capitalization of major U.S. stocks increasing by 13.4 trillion RMB [5]. - There were unusual trading activities, particularly in S&P 500 call options, which yielded returns as high as 2100%, raising suspicions of insider trading [5][7]. - The article draws parallels between Trump's actions and past incidents involving Elon Musk, suggesting a pattern of market manipulation that could harm ordinary investors [8][9]. Group 2: Asset Allocation Strategies - The article introduces a "pyramid model" for asset allocation, consisting of three layers: a stable "defensive layer," a growth-oriented "offensive layer," and a liquid "emergency layer" [11][12][14]. - The defensive layer should comprise 50%-60% of total assets, focusing on stable investments that generate consistent cash flow [15][16]. - The offensive layer, accounting for 20%-30% of assets, aims to capture excess returns from industry transformations, emphasizing the importance of high-dividend stocks [17][18]. - The emergency layer, representing 10%-20% of total assets, is crucial for maintaining liquidity and avoiding forced sales during market downturns [21][57]. Group 3: Recommended Asset Types - Gold is highlighted as a favored safe-haven asset, with central banks purchasing over 1000 tons annually, supporting its price increase [24]. - Bonds are also recommended for their stability during market volatility, with certain bond funds showing resilience amid recent market fluctuations [28][30]. - High-dividend stocks, particularly in sectors like utilities and banking, are suggested for their ability to provide steady income and withstand market pressures [36][39]. Group 4: Future Market Outlook - The article warns of an "asset scarcity" era due to declining interest rates, necessitating a shift in investment strategies to adapt to changing market conditions [60][61]. - It emphasizes the importance of continuously adjusting investment models to seize opportunities in a dynamic financial landscape [63].

Group 1 - The core viewpoint emphasizes that AI is transforming asset allocation by achieving computational equity and enhancing investment research capabilities, particularly through the DeepSeek model [1][11][14] - AI is positioned as an "enhanced tool" in investment research, improving data processing efficiency and optimizing strategy execution, but it cannot fully replace human judgment in complex market scenarios [2][3][21] - The report highlights the importance of integrating AI with traditional investment frameworks to enhance decision-making processes while maintaining human oversight [20][21][30] Group 2 - The application of DeepSeek in asset allocation involves a systematic optimization of traditional strategies, focusing on risk parity and dynamic market timing [16][17][18] - The report discusses the integration of AI in A-share market strategies, utilizing macroeconomic indicators and sentiment analysis to enhance market timing and sector rotation [17][18] - AI's role in ESG investment is explored, demonstrating how it can dynamically incorporate ESG factors into asset allocation frameworks to balance returns and sustainability [18][19] Group 3 - The report outlines the deployment of AI in hedge funds and quantitative trading, showcasing how AI-driven models can assist in decision-making and strategy development [49][54][58] - It emphasizes the need for specialized AI models tailored to financial tasks, such as the Fin-R1 model, which is designed for complex financial reasoning and can be deployed on consumer-grade hardware [65][66] - The success of AI-driven investment strategies is illustrated through case studies, including Minotaur Capital, which achieved significant returns using AI for stock selection [70][74]