- The report systematically reviews 80 new quantitative finance-related research papers in May 2025, covering areas such as equity research, fixed income, fund studies, asset allocation, machine learning applications, and ESG-related studies [1][2][3] - Equity research includes studies on fundamental factors, price-volume and alternative factors, factor research, active quantitative strategies, and other categories, exploring investor behavior biases, asset pricing models, market structure distortions, prediction model innovations, and corporate resilience mechanisms [2][10] - Fixed income research focuses on high-frequency inflation forecasting, sovereign risk premium decomposition, and stochastic interest rate model innovations, with findings such as weekly online inflation rates predicting yield curve slope factors and semi-Markov-modulated Hull-White/CIR models achieving semi-analytical pricing for zero-coupon bonds [22][23] - Fund studies investigate fund selection factors, fund style evaluation, and behavioral biases, revealing strategies like liquidity picking driving excess returns and public pension funds underperforming benchmarks due to alternative investment errors post-2008 [28][30] - Asset allocation research explores multi-asset portfolio management paradigm shifts, systematic currency management, and volatility connectedness constraints, demonstrating dynamic adaptation mechanisms and enhanced performance during crises [32][33][35] - Machine learning applications in finance include innovations in volatility forecasting, credit risk prediction using GraphSAGE models, and long-memory stochastic interval models, significantly improving prediction accuracy and economic value [36][38][40] - ESG-related studies analyze green innovation drivers, ESG evaluation distortions, and corporate environmental response strategies, highlighting mechanisms like family business constraints on green innovation and AI-driven manufacturing green transformation [42][43][45]

Core Viewpoint - Nvidia's recent investment in quantum computing, particularly in PsiQuantum, signifies a strategic move to enhance its position in the rapidly evolving quantum technology landscape, aiming to integrate quantum capabilities with its existing GPU architecture [4][5][11]. Group 1: Nvidia's Quantum Computing Strategy - Nvidia's CEO Jensen Huang initially projected a 20-year timeline for practical quantum computers but later retracted this statement, acknowledging a misjudgment and announcing the establishment of a quantum research center [4][10]. - The company is engaging in late-stage investment negotiations with PsiQuantum, participating in a $750 million funding round led by BlackRock, which would elevate PsiQuantum's post-investment valuation to $6 billion [5][9]. - This investment aligns with Nvidia's strategy of leveraging small investments for high leverage in the potential trillion-dollar quantum market [16]. Group 2: PsiQuantum Overview - Founded in 2016, PsiQuantum has become the highest-valued quantum startup, with a valuation exceeding $3 billion in 2021 and potentially reaching $6 billion with the latest funding [6][9]. - The founding team, primarily from the University of Bristol, has a strong background in quantum research, focusing on scalable, fault-tolerant quantum computing using photonic technology [7][8]. - PsiQuantum's approach aims to transition laboratory technology into mass-produced products, setting it apart from other quantum startups [8][10]. Group 3: Market Dynamics and Competition - The global quantum computing market is currently dominated by superconducting technology, which accounts for 62% of the hardware market, while PsiQuantum's photonic approach represents a unique alternative [18]. - The quantum computing sector is experiencing significant growth, with the Chinese market projected to reach 11.56 billion yuan by 2025, growing at an annual rate exceeding 30% [19][20]. - Major tech companies like IBM, Google, and Microsoft are also heavily investing in quantum computing, indicating a competitive landscape where Nvidia must innovate to maintain its market position [12][13]. Group 4: Government Support and Future Prospects - PsiQuantum has established strong relationships with various governments, securing funding for quantum projects, including a $940 million investment from the Australian government for deploying commercial quantum computers by 2029 [10][15]. - The Chinese government has recognized quantum technology as a core area for development, with multiple provinces outlining support for quantum initiatives in their 2025 work reports [20][21]. - The ongoing competition between photonic and superconducting technologies will be crucial for the future of quantum computing, with companies needing to balance technological breakthroughs with practical applications [21].

内容概要：近年来，随着国民收入水平不断提高，消费者对珠宝首饰的需求逐渐增加，不仅注重产品的 实用性，更追求品牌、设计和个性化，电子商务平台为消费者提供了丰富多样的珠宝选择，满足了不同 层次消费者的需求，此外，随着互联网的普及和电子商务的发展，消费者越来越习惯在线上购物，尤其 是年轻一代消费者，他们更倾向于通过网络购买珠宝首饰，享受便捷的购物体验，在此背景下，我国珠 宝电子商务市场迅速崛起，据中国珠宝玉石首饰行业协会数据显示，2023年我国珠宝电子商务零售额达 3397.5亿元，同比增长26.21%，2024年，在我国商品零售市场增速放缓的不利背景下，金银珠宝作为非 必需品，与整体市场走势同频共振，其降幅居所统计的全部商品类别的首位，据中国珠宝玉石首饰行业 协会数据显示，2024年中国珠宝电子商务零售额降至2982.6亿元。 上市企业：曼卡龙（300945）、迪阿股份（301177）、老凤祥（600612）、中国黄金（600916）、航民 股份（600987）、菜百股份（605599）、豫园股份（600655）、萃华珠宝（002731）、周生生 （00116.HK）、潮宏基（002345）、明牌珠宝（00257 ...

Upstart (UPST) 2025 Conference Summary Company Overview - **Company**: Upstart (UPST) - **Industry**: Consumer Finance and Payments Key Points and Arguments AI and Machine Learning - Upstart has been utilizing AI and machine learning techniques for many years, distinguishing itself from other lenders who have recently adopted similar technologies [5][6] - The competitive advantage of Upstart lies in its speed and the extensive investment in a specialized technology team of approximately 70 machine learning researchers [8][9] - Upstart's models are continuously improved, achieving a 2-3% enhancement in model accuracy each month through investments in model architecture, new consumer data, and computational resources [21][22][23] Macro Resilience and Calibration - Upstart's models have been adjusted to be more macro-aware, allowing them to respond to current economic conditions rather than relying solely on historical data [27][28] - The introduction of the Upstart Macro Index (UMI) helps assess the likelihood of defaults based on current macro conditions, improving the model's calibration speed from eight quarters to as little as two quarters [29][32] Financial Performance and Credit Risk - Credit performance is the key performance indicator (KPI) for Upstart, directly influencing capital market confidence and borrower approvals [36] - The company has established forward flow agreements with private funds, which involve extensive due diligence to ensure credit performance during economic stress [39][40] - Upstart manages risk by creating a macro insurance layer, where overperformance in benign periods compensates for underperformance during economic downturns [61][62] Product Diversification - Upstart is expanding its product offerings to include auto loans, HELOCs, and small dollar loans, driven by borrower needs and market opportunities [42][43] - The strategy aims to serve borrowers throughout their credit lifecycle, leveraging existing data to enhance underwriting accuracy across various products [46][48] Market Conditions and Economic Outlook - The current economic environment shows a disconnect between consumer spending and financial security, with many Americans relying on cash flow products to manage expenses [66][71] - Despite a resilient labor market, there is a significant portion of the population that feels financially insecure, which could impact future credit performance [70][71] Additional Important Insights - Upstart's focus on continuous improvement in its models and the ability to adapt to macroeconomic changes positions it favorably for future challenges [12][30] - The company has made strategic decisions to protect its core product offerings during economic stress, which are now showing positive momentum [50][51] - The leadership team expresses excitement about the potential of new products and their ability to meet diverse consumer needs [52][56]

"汽车正在从纯粹的机械设备，转变为某种像是'装上轮子的计算机'。" 点击阅读：《 巨头Baillie Gifford旗舰基金掌舵人的年度信：在不确定环境中，韧性并不是次要美德， 而是长期成功的核心…… 》 "没有哪个电动车市场像中国这样：极其拥挤，竞争激烈，但它也有望以良好的品质和极低的价格，引 领全球电动车行业。" 地平线是成立于 2015 年的中国 AI 芯片独角兽，专注于为自动驾驶、智能驾驶和通用机器人提供软硬 件一体化的计算平台。 2024 年 10 月 24 日，地平线登陆港股主板市场，成为港股当年最大科技 IPO 。今年 2 月底其股价 曾一度超 10 港元，目前最新市值近千亿港元。 2024 年公司收入 23.84 亿元，同比增长 53.6% ；授权及服务收入增长 70.9% ，毛利率高达 92% ，这意味着公司不仅在卖芯片，还在往更高附加值的软件授权、全栈解决方案转型。 Baillie Gifford 是地平线的主要机构投资者之一。早在私募融资阶段，该公司即已投资地平线，并在 IPO 中认购约 5.07 亿股，投资约 2.6 亿美元，成为最大基石投资者之一。截至 2025 年 4 月，贝利 · ...

大唐黄金(08299)发布公告，公司与无锡专心智制科技有限公司(无锡专心智制)已于香港成立合资公司， 即人工智能矿业有限公司。合资公司由公司拥有51%的股权，并将于集团的综合财务报表中作为附属公 司入账。 无锡专心智制在中国成立的领先的创新科技公司，专注于为工业领域提供数据服务和人工智能(AI)解决 方案。 合资公司旨在开发及评估AI在有色金属勘探、开采工艺以及安全生产方面的应用。合资公司将通过随 机森林、强化学习、卷积神经网络及其他机器学习(ML)工具开发专业的AI应用模型，以推动采矿业向 数字化及智能化转型。此外，合资公司将引入数字孪生及数字化绩效运营系统，整合技术、供应链及运 营要素，以提高生产及资源利用效率以及工作安全，以及为采矿业创造合作机会。 合资公司近期已与全球领先的矿业技术及咨询公司SRK Consulting(China)Ltd.(SRK)订立一份谅解备忘 录，旨在开展战略合作以共同打造AI深度结合黄金及有色金属开采的标杆案例，建立系统性AI采矿学 习机制，开发适用于采矿生产各个环节的应用模型，并于采矿行业中实施AI模型。 公告称，与SRK的战略合作符合当前采矿行业应用AI解决方案的趋势，并 ...

Core Viewpoint - The article highlights significant research contributions from Chinese scholars published in the prestigious journal Cell, focusing on advancements in AI-driven protein activation, nuclear stress bodies' role in inflammation regulation, and a novel bile acid's impact on glucose homeostasis [2][4][15]. Group 1: AI-Driven Protein Activation - A research team from Peking University developed a machine-learning-assisted platform called CAGE-Prox vivo for precise protein activation in living mice, enabling real-time biological studies and therapeutic interventions [4][7]. - The platform allows for the temporary blocking of target protein functions and can be triggered by small molecules, facilitating specific control over protein-protein interactions [7]. Group 2: Nuclear Stress Bodies and Inflammation - A study by the Chinese Academy of Sciences explored the assembly and function of nuclear stress bodies (nSB) under stress conditions, revealing their role in enhancing the transcription of NFIL3, which suppresses inflammatory responses [8][9]. - The research indicates that the expression of NFIL3 is positively correlated with the survival rates of sepsis patients, suggesting a potential therapeutic target for precise diagnosis and treatment of sepsis [12][13]. Group 3: Microbial Bile Acids and Glucose Homeostasis - A collaborative study identified a novel bile acid receptor, MRGPRE, activated by a microbial amino-acid-conjugated bile acid, tryptophan-cholic acid (Trp-CA), which improves glucose regulation [15][18]. - The findings reveal a new mechanism for GLP-1 secretion regulation via MRGPRE, providing insights for developing new diabetes medications without the side effects associated with traditional bile acids [18].

图片来源： Rillet 对于会计部门而言，总账系统是最为关键的软件。作为汇总所有财务交易的核心枢纽，它提供了生成 准确财务报表所需的基础数据。本周三，Rillet 宣布完成 2500 万美元 A 轮融资，由红杉资本领投， 现有投资者跟投。 此次融资距该公司从 First Round Capital、Creandum 和 Susa Ventures 获得 1350 万美元种子轮及 Pre- seed 轮融资仅过去 10 个月。 "总账是财务职能的核心，要求企业更换总账系统无异于进行心脏直视手术。"红杉资本合伙人 Julien Bek 表示。 就在几年前， Bek 还认为风投机构不敢投资开发新型总账软件的初创公司。他解释道，这不仅要克服 客户更换现有会计软件的阻力，建立新的总账业务本身也极具挑战性。 当 Bek 发现成立三年的 Rillet 公司时，他改变了看法。 该公司运用机器学习和生成式 AI 实现会计报 告自动化，直接从客户银行及 Salesforce 、 Stripe 、 Ramp 、 Brex 和 Rippling 等平台提取数据， 生成包括资产负债表和利润表在内的核心财务报表。 " 我认为他们三分之 ...

Global EDA Industry Performance - The global EDA industry is projected to grow by 11% year-on-year in Q4 2024, reaching $4.9 billion, despite a weak performance in the Chinese market [3][4] - The EDA software industry is characterized by high technical barriers, talent reserves, user collaboration, and significant capital scale, with a market concentration exceeding 70% among the top three companies: Cadence, Synopsys, and Siemens EDA [5] Growth Drivers in EDA - The increasing demand for edge computing and high-performance computing (HPC) chips is driving the need for more complex and automated EDA solutions [6] - The rise of cloud solutions facilitates seamless collaboration and enhances accessibility for global design teams [6] - The integration of AI and machine learning algorithms into workflows is optimizing design accuracy and efficiency, reducing costly errors, and accelerating time-to-market [6] Segment Performance - CAE (Computer-Aided Engineering) revenue grew by 10.9% to $1.6969 billion [7] - IC physical design and verification saw a 15.4% increase, reaching $797.9 million [7] - PCB & MCM (Printed Circuit Board & Multi-Chip Module) revenue increased by 15.9% to $476.2 million [7] - Semiconductor IP (SIP) revenue grew by 7.9% to $1.7607 billion, with some companies reporting declines [7] - Service revenue increased by 11% to $195.6 million, reflecting strong design demand amid talent shortages [7] - IC packaging design revenue surged by 70%, indicating a significant rise in advanced packaging demand [7] AI's Role in EDA - EDA vendors are leveraging AI to optimize software engines, processes, and workflows, which is crucial for scalable and reliable outcomes [8] - AI applications in EDA include automating repetitive tasks, enhancing design optimization, and providing intelligent assistance through generative AI [11][12] - AI-driven tools can significantly reduce design cycles and improve accuracy, as demonstrated by Synopsys' AI-driven EDA tools [11] Future Outlook - The emergence of Chiplet technology is transforming chip design and manufacturing paradigms, necessitating new tool support for architecture exploration and signal integrity analysis [13] - EDA tools must evolve to support heterogeneous integration design, with companies like Synopsys and Cadence developing specialized tool suites for Chiplet design [13][15] - The collaboration between EDA tools and IP design capabilities will be critical for future competitiveness, as traditional IP markets face saturation [14]

Core Viewpoint - The research introduces a machine-learning-assisted strategy called CAGE-Prox vivo for precise protein activation in living organisms, providing a universal platform for time-resolved biological studies and on-demand therapeutic interventions [1][13]. Group 1: Research Background - The study emphasizes the importance of gain-of-function research in understanding biological processes and disease pathology, highlighting various protein engineering techniques that have been developed to manipulate proteins [4]. - Current techniques, while effective, often rely on complex protein constructs that may alter the natural function of target proteins [4][5]. Group 2: CAGE-Prox Strategy - CAGE-Prox is a more universal strategy for controlled activation of a wide range of protein targets, independent of the amino acid residue type at the active site [5]. - The strategy utilizes a light-degradable tyrosine residue (ONBY) to temporarily mask protein activity, allowing for high temporal resolution in studying stimulated cellular processes [5][6]. Group 3: CAGE-Prox vivo Development - The CAGE-Prox vivo strategy incorporates a non-natural amino acid, trans-cyclooctene-tyrosine (TCOY), which can be introduced near the active site of target proteins to temporarily deactivate their function [7][9]. - The research team developed an integrated machine learning process to evolve an aminoacyl-tRNA synthetase (aaRS) that can efficiently incorporate TCOY into proteins [10][11]. Group 4: Applications of CAGE-Prox vivo - The CAGE-Prox vivo system enables precise killing of tumor cells by temporarily inactivating the anthrax lethal factor (LF) and then restoring its activity through a small molecule-triggered bioorthogonal reaction [9][10]. - The strategy also allows for the construction of safer bispecific antibodies that only regain their tumor-targeting function upon specific chemical activation, reducing the risk of cytokine storms and related toxicities [11][12].