From Text to Evidence: LLMs in Economics and Finance

We review applications of large language models (LLMs) in economic and financial research, focusing on text analysis and variable construction, forecasting and decision support, simulation of economic agents, and research assistance and automation. We examine what researchers use LLMs to do, how the reported results compare with existing methods, and what limits their use. Contextual interpretation improves several classification tasks, but variable construction and measurement error affect subsequent analysis. Some forecasts improve on benchmarks, although gains vary and predictive signals need not yield trading profits. Human observations help calibrate agents and improve predictions of survey responses. Automated systems generate economic mechanisms and connect empirical analysis with drafting, while researchers still judge questions and explanations. We then discuss future directions for improving research quality and making better use of researchers' expertise.