Researchers publish study on reconstructing economic policy uncertainty index using large language models
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Researcher Ethan Hartley released a study on reconstructing economic policy uncertainty indices using large language models, finding that LLMs fine-tuned on human-audited labels identify nearly twice as many audited flagged articles as traditional keyword methods, and are robust to noisy text and language drift. The study also notes that the choice of classifier, threshold, and aggregation method significantly affects the index's performance and meaning, thereby altering the relationship between policy uncertainty and returns.
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- arXiv q-finFrom Narrative to Numbers: Large Language Models and Economic Policy Uncertainty
This study examines the impact of adopting machine-generated data in economic analysis, taking the Economic Policy Uncertainty index as the research object. It finds that large language models fine-tuned on human-audited labels identify nearly twice as many audited marked articles as keyword-based alternative methods, and are robust to noisy text and language drift. The choice of classifier, threshold, and aggregation method significantly affects index performance and interpretation, thereby altering the relationship between policy uncertainty and returns.
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