Insurance analytics involves the use of data analysis and statistical techniques to gain insights into the insurance industry. It helps insurers make informed decisions, assess risks, detect fraudulent activities, and enhance overall operational efficiency. This technology leverages data from various sources, including customer information, claims data, and market trends, to optimize underwriting, pricing, and claims processing activities.

Global predictive AI in stock market encompasses the application of advanced computational technologies, including machine learning, deep learning, and natural language processing, to analyze vast and complex financial datasets. This technology processes historical price data, corporate filings, macroeconomic indicators, and real time news sentiment to identify patterns and forecast future stock price movements, market trends, and volatility. The primary objective is to generate data driven, actionable insights that enhance algorithmic trading strategies, optimize portfolio management, and improve risk assessment frameworks. This market represents a significant shift from traditional technical and fundamental analysis toward more sophisticated, automated, and probabilistic models for investment decision making.

Predictive AI in retail market refers to the application of advanced analytical technologies, including machine learning, statistical algorithms, and data mining, to analyse historical and real time data. The primary objective is to forecast future events, consumer behaviours, and market trends with a high degree of accuracy. This technology enables retail organizations to transition from a reactive to a proactive operational stance by anticipating outcomes related to sales, inventory requirements, and customer preferences. By processing vast datasets that encompass transaction histories, customer interactions, and external market factors, predictive AI provides actionable insights. These insights empower retailers to make informed, data driven decisions across various functions such as merchandising, supply chain management, marketing, and customer service, ultimately aiming to enhance operational efficiency, minimize risks like overstocking or stockouts, and personalize the customer journey to foster loyalty and drive revenue growth.

The market by value in this report is defined as the revenues that enterprises gain from goods and/or services sold within the specified market and geography. • Values in this market are ‘factory gate’ values, that is the value of goods sold by the manufacturers or creators of the goods, whether to other entities (including downstream manufacturers, wholesalers, distributors and retailers) or directly to end customers. The value of goods in this market includes related services sold by the creators of the goods.

2026 年美国经济状况或比表象更为脆弱，经济增长的驱动局限在 AI 相关的投资和高收入群体的消费，其他部分贡献甚微。失业率在供需两端均受限的情况下艰难维持平衡，但劳动力市场周期性放缓的脚步仍未停歇。通胀 向目标回落的进程在核心分项牵制下也将放缓。在此背景下，美联储的决策变得异常复杂，短期出于对劳动力市场的担忧将选择继续降息，但空间有限，目标点位应在略低于名义中性利率的水平，而且随着时间推移，美联储主席人选对于降息预期的扰动也将愈发明显。另外，其他政策上的拉锯也仍在进行，上半年聚焦财政预算和关税判决的落地，下半年关注特朗普能否在中期选举中保住两院的优势地位。欧洲方面，2026 年经济面临的外部风险偏向下行，更多挑战来源于经济内生动能的修复，财政支持下的国防开支将使得欧元区投资重获动能。在通胀回归基本无虞的情况下，欧央行政策不会有较大改变，预计全年仅有一次降息。

主题形态学，希望打造一把针对主题投资的利器。1）识别主题机会：对主题指数进行全面、客观的前期筛选，使投资经理更专注于逻辑分析、空间判断、投资决策等主动研究。2）实现主题“指数”的可投资：映射股票、转债，构建可投资的权益+组合。而且提供主题指数的板块分类、机构持仓等辅助指标。3）本文为形态学三板斧的第一篇：右侧突破。捕捉主题启动信号，参与行情的第一波。

文章的核心内容主要围绕多样化驱动的大型股票需求，通过理论分析和实证检验，探讨了投资者，尤其是机构投资者，在多样化其投资组合时对大市值股票的偏好：研究发现，随着投资组合的价值集中度提高，主动管理型投资组合通常会有意识地削减重仓头寸，以维持一定水平的实际多元化。这种再平衡过程受到监管指引和基金自身风险管理历史所设定的隐性阈值的影响。由于大盘股通常在投资组合中占据较大权重，并被广泛持有，它们常常成为这种风险管理模式下协同逆向交易需求的目标。由多元化需求驱动的这种行为，在大盘股投资组合中形成了一种独特的收益反转模式。这种需求所带来的补偿效应在现代样本期（1990 年至 2022 年）中，进一步加剧了动量收益的表现。

The global agentic AI for financial services market encompasses intelligent systems and platforms capable of autonomous action to achieve complex, predefined objectives within the financial industry.

展望 2026 年，蓦然回首 2025 年上半年市场定价接近 40bp 的降息空间当前只兑现了 25%，对于明年“股强债弱”的大方向上，似乎市场预期非常一致，但这种一致也隐含了较多的危险：大家的逻辑都不太一样，甚至完全相反，但竟不影响大方向的结论。我们暂且抛开“年底过度的一致预期都反转很快”，明年决定利率走势的核心因素是什么？或者说，到底应该以何种方式去应对 2026 年的债券波动？

在过去的 4 周时间里（11 月 8 日发布 9 月全社会债务数据综述以来），国内股债双杀，虽然基本符合我们之前有关宏观流动性边际收敛风险不断增加的判断，但债券的跌幅超预 期，我们倾向于认为，这主要与万科的事件性冲击有关，类似 2020 年 11 月的永煤事件。最新比较超预期的数据来自于盈利，我们作为盈利代理变量的私人部门负债增速在 10 月跌破了前低，我们目前仍维持盈利低位震荡的判断，但风险有所上升。展望 12 月，我们重点关注盈利能否在 10-11 月的低位出现企稳甚至反弹，宏观流动性大概率继续边际收敛，风险偏好亦大概率继续下降，股债性价比偏向债券和权益风格偏向价值的趋势不变，尚难确定的是价值和长债的比较，因此从资产配置的角度，我们继续推荐长债加价值的组合。特别是债券，提请投资者关注万科事件冲击形成超调后的介入机会。