Core Viewpoint - Financial One Account, a fintech company under Ping An, is set to delist from both the NYSE and HKEX following its privatization plan, which has been approved by the Grand Court of the Cayman Islands [1][2][4]. Group 1: Delisting and Privatization - Financial One Account has withdrawn its listing status from HKEX as of November 21, with the final delisting process from NYSE to be completed by December 1 [1]. - The privatization offer was made by its controlling shareholder, Platinum Yu Limited, which is a subsidiary of Ping An Group, proposing to acquire all issued shares at HKD 2.068 per share, representing a premium of approximately 23.10% over the last closing price [2][3]. - Following the privatization, Ping An's ownership in Financial One Account will increase to 100% [2]. Group 2: Financial Performance and Market Conditions - Since its listing in December 2019, Financial One Account's stock price has dropped over 95%, attributed to low liquidity, reduced investor interest, and insufficient coverage by brokerage reports [6][7]. - The company's revenue from ongoing operations is projected to decrease by approximately 36.16% from FY2023 to FY2024, leading to increased operating losses [7]. - Financial One Account has faced challenges in diversifying its revenue sources, remaining heavily reliant on Ping An for income, which has been a significant concern for investors [6][7]. Group 3: Strategic Direction Post-Privatization - After privatization, the company plans to focus on long-term strategic goals, including enhancing customer relationships, optimizing products, and expanding its ecosystem and overseas business [7][8]. - The controlling shareholder, Platinum Yu, aims to inject additional financial resources and integrate the company further into the broader Ping An ecosystem once it becomes a wholly-owned subsidiary [8].

Market Overview - A-shares showed a rebound on November 24, with all three major indices closing in the green and over 4,200 stocks rising. However, market sentiment has slightly declined, with a trading volume of 1.74 trillion yuan, down by 2.4 billion yuan compared to the previous day [1] Sector Performance - The technology growth sector significantly recovered, with the defense and military industry emerging as a leading "super dark horse," attracting over 13.3 billion yuan in net inflows, the highest across all industries [2][4] - The defense military ETF (512810) surged by 3.78%, marking its largest single-day increase since July, while the general aviation ETF (159231) rose by 3.43%, both achieving their highest daily trading volumes in months [2][4] - The financial technology sector rebounded in the afternoon, with the largest financial technology ETF (159851) gaining 2% and maintaining its position above the annual line. The AI application by Alibaba, "Qianwen," achieved over 10 million downloads in just seven days, setting a record for AI application growth [3][14] Investment Insights - Analysts from Guotai Junan Securities believe that the Chinese stock market will gradually stabilize and embark on a year-end rally, despite recent global market fluctuations [4] - The defense military sector is expected to see increased investment due to ongoing geopolitical tensions, which may drive high-quality development in the industry [7] - The Hong Kong AI sector is showing strong momentum, with major stocks like Alibaba and Kuaishou experiencing significant gains, indicating a recovery phase for the sector [10][12] ETF Highlights - The Hong Kong Internet ETF (513770) has a current price-to-earnings ratio of 21.93, placing it in the historical bottom range, indicating a valuation advantage compared to other indices [10][11] - The financial technology ETF (159851) has a scale exceeding 90 billion yuan, with an average daily trading volume of 8 billion yuan over the past six months, making it a leading choice among similar ETFs [17]

Core Insights - Several banks, including Agricultural Bank of China, Minsheng Bank, Bank of China, and others, have initiated recruitment for postdoctoral researchers for 2026, focusing on strategic research and innovation in the financial sector [1][3][4] - The recruitment emphasizes interdisciplinary backgrounds, particularly in artificial intelligence, big models, and financial technology, reflecting a trend towards integrating technology with traditional finance [5][6] Recruitment Conditions - The recruitment criteria have become stricter, with banks favoring candidates with interdisciplinary and practical financial experience [3][4] - Specific requirements include a PhD obtained within the last three years or expected graduation by July 2026, with an age limit of 35 years [3][4] - Preferred fields of study include economics, finance, statistics, applied mathematics, computer science, big data, and artificial intelligence, with a strong emphasis on research capability and dedication [3][4] Research Focus - The research topics set by banks highlight their strategic priorities, with a strong focus on AI, big models, and financial technology applications [6][7] - Examples of research topics include "AI Empowering Digital Transformation of Commercial Banks" and "Application of AI and Big Model Technology in Risk Management," indicating a direct response to industry needs [6][7] Industry Trends - The recruitment surge is driven by the dual pressures of transformation in the banking sector and technological opportunities, with banks seeking to enhance core competitiveness through technology [7][8] - AI technologies are seen as crucial for improving operational efficiency and risk management, which are key demands in the current banking landscape [7][8] - The focus on practical research outcomes distinguishes bank postdoctoral programs from academic institutions, emphasizing the need for research that addresses real business challenges [7][8]

Core Insights - The 19th Shenzhen International Financial Expo concluded with significant participation and growth in various metrics, indicating a strong industry presence and influence [1] Group 1: Event Overview - The expo featured 288 participating institutions, representing an 80% year-on-year increase [1] - Total attendance reached 47,400, marking a 58% increase in visitor numbers compared to the previous year [1] - Over 50 thematic activities were held, showcasing 18 new industry policies and product outcomes [1] Group 2: Financial Technology Focus - More than 30 financial technology companies participated, highlighting the latest cutting-edge technologies and innovations [1] - Huawei collaborated with 12 ecosystem partners to present advanced financial ICT infrastructure products and solutions [1] Group 3: Investment and Funding Highlights - The expo included 33 supply-demand matching sessions and investment project roadshows, with some projects attracting investment intentions totaling 1.055 billion yuan [1] - A dedicated exhibition area for government-guided funds was established, showcasing 29 star enterprises and 45 innovative companies, including "little giants" and unicorns [1]

Core Insights - The seminar titled "Large Model Technology Ecosystem and Application Practice" was held at the Chinese University of Hong Kong (Shenzhen) to educate students on the intersection of technology and finance [1][2] - The session was led by a senior architect from the AI development team at China Merchants Securities, focusing on the principles of large model technology and its applications in financial scenarios [1][2] Group 1: Theoretical Framework - The lecture began with an introduction to the basic concepts and development history of large models, covering core architectures, training processes, and key technologies [2] - It included an analysis of typical models such as Stable Diffusion and Transformer, explaining the production steps and reasoning optimization methods of large language models [2] Group 2: Practical Applications in Finance - The speaker highlighted various applications of AI in the financial sector, including intelligent investment advisory, investment decision-making, and AI-native applications [3] - International case studies were shared to illustrate how large models are reshaping the business logic and ecological structure of the securities industry [3] Group 3: Career Development and Future Initiatives - The seminar provided career development advice and recommended quality learning resources for students aspiring to enter the fintech field [3] - China Merchants Securities plans to continuously improve course content and teaching methods to build a solid learning and practice platform for fintech talent [3]

Group 1: Financial Technology ETF Performance - The China Securities Financial Technology Theme Index (930986) increased by 2.57%, with constituent stocks such as Hengyin Technology (603106) rising by 9.96%, Xinzhisoft (688590) by 8.87%, and Puyuan Information (688118) by 8.28% [1] - The Financial Technology ETF Huatai-PineBridge (159103) rose by 2.46%, with the latest price at 0.88 yuan [1] - The trading volume for the Financial Technology ETF Huatai-PineBridge was 593.39 million yuan, with a turnover rate of 2.16% [1] Group 2: Fund Share Growth - The Financial Technology ETF Huatai-PineBridge saw a significant increase of 15 million shares over the past two weeks, ranking second among comparable funds [3] Group 3: Capital Inflow - The latest net capital inflow for the Financial Technology ETF Huatai-PineBridge was 3.44 million yuan, with a total of 13.38 million yuan accumulated over the last ten trading days [4] Group 4: AI Developments - Google recently updated its next-generation multimodal AI model, Gemini 3.0 Pro Image, which is designed to tackle challenging image generation tasks and is now available on the Vertex AI platform [4] - The Gemini 3 model, released on November 18, has achieved top rankings in multiple benchmark tests, marking a significant advancement in AI capabilities [5] - Major tech companies, including Amazon, Microsoft, Google, and Meta, collectively invested over 110 billion dollars in AI-related infrastructure in Q3 2025, indicating strong confidence in the long-term development of AI [5] Group 5: Financial Technology Sector Outlook - The financial technology sector is expected to benefit from a strong liquidity environment, with internet brokerage firms showing greater elasticity in bullish market conditions [6] - The Financial Technology ETF Huatai-PineBridge covers a wide range of high-elasticity sectors, including internet brokerages, financial IT, AI applications, and cross-border payments, driven by both policy and technological advancements [6]

11月24日，跨境通盘中上涨2.01%，截至13:53，报4.57元/股，成交1.77亿元，换手率2.55%，总市值 71.20亿元。 今年以来跨境通已经22次登上龙虎榜，最近一次登上龙虎榜为9月16日，当日龙虎榜净买入2.57亿元； 买入总计4.24亿元 ，占总成交额比13.02%；卖出总计1.67亿元 ，占总成交额比5.12%。 资料显示，跨境通宝电子商务股份有限公司位于山西省太原市万柏林区滨河西路51号摩天石3#28F，成 立日期2003年3月7日，上市日期2011年12月8日，公司主营业务涉及跨境出口电商业务、跨境进口电商 业务。主营业务收入构成为：母婴用品等92.33%，服饰家居类7.67%。 跨境通所属申万行业为：商贸零售-互联网电商-跨境电商。所属概念板块包括：金融科技、数字经济、 无线耳机、跨境电商、电子商务等。 资金流向方面，主力资金净流入462.74万元，特大单买入1088.35万元，占比6.16%，卖出386.17万元， 占比2.19%；大单买入2509.12万元，占比14.20%，卖出2748.56万元，占比15.55%。 跨境通今年以来股价涨19.01%，近5个交易日跌6.54%，近 ...

Core Points - The stock price of Xinyada increased by 2.01% on November 24, reaching 16.72 CNY per share, with a total market capitalization of 7.797 billion CNY [1] - Year-to-date, Xinyada's stock price has risen by 33.62%, with a recent 5-day increase of 2.39% and a 20-day decrease of 0.65% [1] - For the period from January to September 2025, Xinyada reported a revenue of 1.391 billion CNY, representing a year-on-year growth of 9.02%, and a net profit attributable to shareholders of 22.475 million CNY, up 492.43% [2] Financial Performance - Xinyada's main business revenue composition is 99.30% from the IT industry, 0.65% from the environmental protection industry, and 0.05% from other business revenues [1] - The company has distributed a total of 826 million CNY in dividends since its A-share listing, with 143 million CNY distributed in the last three years [3] Shareholder Information - As of September 30, 2025, Xinyada had 74,000 shareholders, an increase of 9.83% from the previous period, with an average of 6,189 circulating shares per shareholder, down 8.95% [2] - The fourth largest circulating shareholder is Huabao Zhongzheng Financial Technology Theme ETF, holding 6.2972 million shares, an increase of 3.1255 million shares from the previous period [3]

Core Viewpoint - Nantian Information's stock price has shown fluctuations, with a recent increase of 2.05% but a decline of 16.30% over the past 60 days, indicating potential volatility in the market [1]. Financial Performance - For the period from January to September 2025, Nantian Information achieved a revenue of 7.091 billion yuan, representing a year-on-year growth of 21.90% [2]. - The company reported a net profit attributable to shareholders of -10.8998 million yuan, a significant decrease of 119.49% compared to the previous period [2]. Shareholder Information - As of September 30, 2025, the number of shareholders for Nantian Information was 59,900, a decrease of 20.65% from the previous period [2]. - The average number of circulating shares per shareholder increased by 26.02% to 6,494 shares [2]. Dividend Distribution - Since its A-share listing, Nantian Information has distributed a total of 469 million yuan in dividends, with 102 million yuan distributed over the past three years [3]. Institutional Holdings - As of September 30, 2025, the second-largest circulating shareholder is Huabao Zhongzheng Financial Technology Theme ETF, holding 4.274 million shares, an increase of 1.9471 million shares from the previous period [3]. - Hong Kong Central Clearing Limited has exited the list of the top ten circulating shareholders [3].

11月22日，由《21世纪经济报道》主办的"21世纪金融竞争力案例（2025年度）"评选结果揭晓，小雨点 凭借特色评分产品，从众多案例中脱颖而出，荣获产品类"年度领航案例奖"。 此次荣获产品类"年度领 航案例奖"，是对小雨点深耕金融科技赛道，不断提升科技风控与数据智能应用水平的高度认可。 本次21世纪金融竞争力优秀案例评选，旨在将先进实践经验转化为业务动能，推动行业健康发展。评选 通过专家推荐、机构申报等形式公开进行，经由客观指标和主观指标共同研究确定。 获奖的小雨点特色评分产品涵盖个人与企业信用及反欺诈评分，以灵活、高效、合规为核心特点，广泛 应用于营销画像、信用评估、信息核验与反欺诈等多类金融场景，还在数据融合与模型建设上具有显著 优势。例如在某银行的消费贷款联合建模项目中，该行因线上消费贷业务所使用的第三方数据效果未达 预期，希望拓展数据维度以提升风险识别精准度。小雨点通过深入调研其业务流程与数据使用情况，筛 选合适数据源并开展联合建模，为额度10万元以内、期限1年期的自营客群消费贷产品构建定制化风控 模型。项目通过将授信模型内部规则与定制模型构建交叉矩阵，有效提升了高风险客户的判断精度。项 目成果显示 ...