Core Insights - The article emphasizes the importance of using data to understand corporate revenue distribution and the sensitivity of companies to tariffs, providing a comprehensive view of regional income and potential risks associated with trade policies [3][5]. Group 1: Regional Classification Data - Bloomberg is launching a regional classification fundamental data product to empower investors by providing a detailed view of company revenue by region, utilizing both reported and forecasted data [3]. - The product aims to create a multi-level standardized structure covering various regions and specific countries, offering insights into company revenue distribution [3][5]. Group 2: Sensitivity Scoring - A sensitivity scoring system has been developed to assess companies' exposure to tariff risks based on their revenue distribution across different countries and industries [5][6]. - The top ten companies with the highest tariff sensitivity scores from the Bloomberg U.S. Large Cap Index (B500) have been identified, which helps investors evaluate the potential impact of tariffs on their portfolios [6]. Group 3: Index Comparison - A bottom-up approach is used to compare the sensitivity scores of different indices, revealing that the European index (EMEAP) is most sensitive to current macroeconomic conditions [7][9]. - The analysis provides valuable insights for investors to enhance their risk management capabilities by understanding how different regions and industries are affected by new tariff policies [7]. Group 4: Cost Risk Analysis - The article highlights the importance of considering cost risks alongside revenue risks, particularly in industries like automotive, where tariffs on imported components can significantly impact profit margins [9][10]. - By combining sensitivity scores with supply chain data, investors can gain a deeper understanding of how global trade dynamics affect companies, identifying potential risks and opportunities [9][10]. Group 5: Industry Focus - Automotive - The automotive industry is used as a case study to illustrate how financial data can be leveraged to construct risk/opportunity maps based on profit margins and tariff sensitivity [12][14]. - The analysis of companies like Renault shows that indirect cost risks from suppliers can significantly affect production costs, even if the company itself is not directly impacted by tariff policies [12][15].