强大的新型宏观预测指标助力超额收益

Core Viewpoint - The emergence of new AI predictive models is transforming the way investors approach global macroeconomic forecasting, providing more precise methods for achieving excess returns [2][5]. Group 1: Investment Strategies and Data Management - Financial services are investing unprecedented resources in data management and AI to build more accurate macroeconomic forecasting models, as asset allocation decisions significantly impact portfolio performance [3][5]. - The complexity of global markets and the vast amount of data involved have historically made reliable macroeconomic forecasting a challenging goal for investors [3][5]. - The use of clean and accurate training data, along with techniques like Teacher forcing, enhances the accuracy of AI predictions by ensuring optimal information is used at each step of the training process [3][5]. Group 2: AI and Predictive Models - Many large buy-side and sell-side firms are increasing their investments in data management and AI resources, although traditional methods involving human resources for data handling are costly and difficult to scale [4][5]. - LSEG's global macroeconomic forecasting integrates advanced technology and AI reasoning, providing a range of indicators that help traders and asset managers make informed decisions [7]. - The predictive service can be delivered via APIs, allowing companies to trade based on forecasts before official government data releases, such as the Consumer Price Index (CPI) [7][12]. Group 3: Performance and Insights - LSEG's scalable, centralized data warehouse and automated framework support cutting-edge AI reasoning and automation, enhancing performance when data management and AI work in tandem [6]. - The integration of various data sources, including machine-readable news and point-in-time data, allows for the discovery of complex relationships and the generation of forward-looking economic indicators [5][12]. - Users can access predictive results and related data through a universal platform, enabling them to overlay this information onto existing portfolios for better identification of inter-data relationships [12].