Generative AI for Analysts

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.

Statistical Finance (q-fin.ST); Artificial Intelligence (cs.AI); General Economics (econ.GN); General Finance (q-fin.GN)

arXiv:2512.19705 [q-fin.ST]

(or arXiv:2512.19705v2 [q-fin.ST] for this version)

https://doi.org/10.48550/arXiv.2512.19705 Focus to learn more arXiv-issued DOI via DataCite

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