ChatGPT as a Time Capsule: The Limits of Price Discovery

TL;DR

Frozen large language model checkpoints serve as time-stamped summaries of public textual information, enabling sector-neutral outlook scores that predict future stock returns beyond standard valuation measures.

Contribution

This paper introduces a novel method of extracting sector-neutral outlook scores from LLM snapshots to predict stock returns, highlighting the value of temporal textual data.

Findings

Outlook scores are positively associated with analyst revisions and target-price changes.

Predictability of returns increases with the investment horizon.

Stronger predictability for firms with high analyst coverage.

Abstract

Frozen large language model (LLM) checkpoints extract information from pre-cutoff public text that is associated with future fundamentals and equity returns beyond standard contemporaneous valuation measures. Because each frozen checkpoint has a fixed knowledge cutoff, it can be interpreted as a compressed representation of publicly available textual information at a given point in time. We treat twelve OpenAI snapshots spanning 2021-2025 as time-stamped summaries of the public textual record and extract a sector-neutral LLM outlook score for roughly 7,000 U.S. equities per cross-section. The outlook score is positively associated with analyst revisions, target-price changes and one-month cross-sectional returns in both Fama-MacBeth regressions and pooled panels with model fixed effects (t = 6.02), after direct controls for market-implied valuation and standard factors. Predictability broadly increases with the return horizon, despite a non-monotonic intermediate dip, and, in the pooled panel, is stronger for firms with high analyst coverage, consistent with the view that the bottleneck is not investor inattention but the cost of aggregating dispersed qualitative information across many documents.