The market's mirror: Revealing investor disagreement with LLMS

Vineet Bhagwat, J. Anthony Cookson, Chukwuma Chijioke Dim and Marina Niessner

No 390, Working Papers from The University of Chicago Booth School of Business, George J. Stigler Center for the Study of the Economy and the State

Abstract: Can large language models (LLMs) reveal how investors disagree? We endow an LLM with 216 representative investor personas, elicit buy/hold/sell responses to 1.25 million S&P 500 news headlines, and measure disagreement as within-headline dispersion of responses. Disagreement is highest for socially oriented news and lowest for fundamentals, and predicts elevated same- and next-day abnormal trading volume, even after the LLM's training cutoff. Disagreement rooted in income, education, and age is most strongly related to trading, whereas political disagreement, though largest, is least related. Persona responses reflect non-pecuniary rationales, align with human-survey benchmarks, and are stable across prompt variations.

Keywords: Investor Disagreement; LLMs; Beliefs; Sentiment; Narratives; Trading (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:cbscwp:344253

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