Generative AI for Analysts
q-fin.ST, cs.AI, econ.GN, q-fin.EC, q-fin.GN
Submitted: 2025-12-12
Updated: 2026-09-09
Comments: Revised version with updated analyses, additional robustness tests, and expanded discussion
License: http://creativecommons.org/licenses/by/4.0/
The gist: We study how generative artificial intelligence (GenAI) reshapes financial analysts' information production.
Terminology
Abstract
We study how generative artificial intelligence (GenAI) reshapes financial analysts' information production. Using the 2023 integration of GenAI into FACTSET as a plausibly exogenous change in AI access, we find that FACTSET-associated reports become markedly richer--featuring 26% more distinct information sources, 24% broader topical coverage, and 21% more analytical methods--while also improving timeliness. However, these gains do not uniformly improve decision quality: relative forecast accuracy declines when analysts face greater information-processing demands. Yet, a machine-learning benchmark processing the same observable inputs shows no analogous deterioration, pointing to a human processing constraint rather than poorer underlying information. Placebo tests using other data vendors make a common platform-wide technology trend unlikely. Overall, GenAI relaxes information-acquisition constraints while making human attention a more important bottleneck.
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