美国统计局“无法相信”后，华尔街更依赖“私人调查”：ADP就业、挑战者裁员人数等数据对市场愈发重

Group 1 - The core issue is the growing skepticism among Wall Street investors regarding the reliability of government economic data, particularly after President Trump's dismissal of BLS head Erika McEntarfer and allegations of data manipulation [1][2][3] - Investors are increasingly turning to private data sources such as ADP Research's employment reports and Challenger, Gray & Christmas Inc.'s layoff data as alternatives to government statistics [1][2][3] - Analysts express concerns that if government data becomes politicized, it could undermine market trust and increase risk premiums, leading to a potential decrease in valuations and increased data volatility [3][4] Group 2 - Philip Petursson from IG Wealth Management highlights that the recent turmoil surrounding BLS raises questions about the future effectiveness of government data [2] - Michael O'Rourke from Jonestrading emphasizes the need to focus more on private data sources, particularly ADP Research, due to the current situation [2] - Economists like Brian Jacobsen view private data as a counterbalance to official statistics and plan to monitor Senate confirmation hearings for potential changes in BLS data collection methods [2][3] Group 3 - Julian Emanuel from Evercore ISI notes that as government data becomes increasingly questionable, there is a growing focus on private data sources [3] - Investors believe that private data can help quickly identify issues if government data is politicized, allowing for cross-validation of information [3] - Concerns are raised by Donald Ellenberger from Federated Hermes about the long-term implications of perceived government interference in data reporting, emphasizing the foundational role of trust in the financial industry [3] Group 4 - Despite these concerns, Wall Street is not completely abandoning BLS data, with the upcoming non-farm payroll report on September 5 being a significant market event [4] - There are existing issues with BLS data reliability, including funding shortages, outdated data collection methods, and declining response rates, which have led to increased reliance on estimates [4]