Generative AI for Analysts

Jian Xue; Qian Zhang; Wu Zhu

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arXiv · 2512.19705

Generative AI for Analysts

Jian Xue·Qian Zhang·Wu Zhu

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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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Jian Xue, Qian Zhang, Wu Zhu. 2026-09-09. Generative AI for Analysts. https://arxiv.org/abs/2512.19705

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