A recent Bloomberg study highlights how U.S. economists are shifting from traditional metrics to innovative tools and real-time data sources in response to the fast-paced global market and the upcoming Presidential Election.
Economists Turn to Innovative Tools Amid Rapid News Cycles
In a rapidly changing global market landscape, economists find themselves needing to adapt quickly to shorter news cycles and market events that unfold at an unprecedented pace. Traditional macroeconomic metrics and tools, once the cornerstones of economic analysis, are increasingly viewed as too sluggish to provide timely insights. This evolving scenario is highlighted in a recent study released on October 30 by Bloomberg in partnership with Coalition Greenwich, offering a fresh perspective on how U.S. economists and strategists are employing innovative tools and data in anticipation of significant macro events, particularly the upcoming U.S. Presidential Election.
Michael McDonough, Chief Economist for Financial Products at Bloomberg, underscores the shift in economists’ approach from relying on conventional data sources which often lag behind current circumstances. McDonough notes that during the first presidential debate, the reliance on traditional polling data resulted in delays in impact assessment, spurring economists to seek more immediate analytical tools. “Predictive markets provide a quicker understanding of the potential significance of events, allowing a faster grasp of their implications,” McDonough shared with Traders Magazine.
The study reveals a growing preference among economists for non-traditional data sources, including social media sentiment analysis and real-time consumer transaction data, as well as the utilization of generative AI-enhanced analytics. As traditional economic data appears increasingly retrospective, these alternative approaches are gaining traction as they provide more immediate insights into current economic conditions. The study finds these methods holding the second and third positions as the most important tools for analysing macroeconomic drivers over the forthcoming year.
Adopting new data tools is notably valued by early-career economists who see generative AI as a transformational component of forecasting models expected to evolve over the next few years. Kevin McPartland, Head of Market Structure and Technology Research at Coalition Greenwich, elaborates on the integration of these alternatives into daily workflows among seasoned economists. “In our research, a significant portion of economists with over 15 years of experience are incorporating social media sentiment and real-time consumer data into their processes,” McPartland reports.
Despite the increasing importance of alternative datasets, 56% of experienced economists continue to prioritise point-in-time data over these newer sources when analysing the U.S. economy. Nevertheless, AI finds applications beyond forecasting. The study identifies summarisation tools and sentiment analysis as areas where AI could have a transformative impact on economists’ workflows.
McPartland also highlights how AI can be instrumental in risk analytics, particularly in assessing potential exposures related to election outcomes. As part of the broader adoption of AI and data technology, experts acknowledge the necessity for fast, accurate, and easily accessible data to navigate the overwhelming volume of information now available.
McDonough emphasises the role of AI in integrating and modelling large datasets, enabling economists to extract meaningful insights efficiently. This emerging dependence on AI and alternative data is anticipated to become increasingly crucial for economists and strategists, providing them with the tools necessary to manage the exponentially growing amounts of data they must analyse.
As economists adapt to these changes, the study provides a revealing glimpse into the evolving methodologies poised to redefine economic analysis in a world where immediacy is key.
Source: Noah Wire Services
