Generative AI for Analysts

ArXiv ID: 2512.19705 “View on arXiv”

Authors: Jian Xue, Qian Zhang, Wu Zhu

Abstract

We study how generative artificial intelligence (AI) transforms the work of financial analysts. Using the 2023 launch of FactSet’s AI platform as a natural experiment, we find that adoption produces markedly richer and more comprehensive reports – featuring 40% more distinct information sources, 34% broader topical coverage, and 25% greater use of advanced analytical methods – while also improving timeliness. However, forecast errors rise by 59% as AI-assisted reports convey a more balanced mix of positive and negative information that is harder to synthesize, particularly for analysts facing heavier cognitive demands. Placebo tests using other data vendors confirm that these effects are unique to FactSet’s AI integration. Overall, our findings reveal both the productivity gains and cognitive limits of generative AI in financial information production.

Keywords: Generative AI, Financial Analysts, Forecast Accuracy, Natural Experiment, Information Synthesis

Complexity vs Empirical Score

• Math Complexity: 2.0/10
• Empirical Rigor: 9.5/10
• Quadrant: Street Traders
• Why: The paper employs sophisticated econometric methods (DiD, PSM, entropy balancing) on a large proprietary dataset of analyst reports, but uses minimal complex mathematics or formulas. The analysis is highly data-driven and backtest-ready, focusing on empirical causal inference.

  flowchart TD
    A["Research Goal:<br/>How does Generative AI<br/>transform financial analysts' work?"] --> B["Natural Experiment:<br/>2023 FactSet AI Platform<br/>Launch"]
    B --> C["Data/Inputs:<br/>Analyst Reports<br/>Forecast Accuracy"]
    C --> D["Computational Process:<br/>Difference-in-Differences Analysis<br/>Placebo Tests (Other Vendors)"]
    D --> E{"Key Findings/Outcomes"}
    E --> F["Productivity Gains:<br/>+40% Info Sources<br/>+34% Topical Coverage"]
    E --> G["Cognitive Limits:<br/>+59% Forecast Errors<br/>Harder Synthesis"]