Working Paper: NBER ID: w28800
Authors: Sean Cao; Wei Jiang; Junbo L. Wang; Baozhong Yang
Abstract: An AI analyst we build to digest corporate financial information, qualitative disclosure and macroeconomic indicators is able to beat the majority of human analysts in stock price forecasts and generate excess returns compared to following human analyst. In the contest of “man vs machine,” the relative advantage of the AI Analyst is stronger when the firm is complex, and when information is high-dimensional, transparent and voluminous. Human analysts remain competitive when critical information requires institutional knowledge (such as the nature of intangible assets). The edge of the AI over human analysts declines over time when analysts gain access to alternative data and to in-house AI resources. Combining AI’s computational power and the human art of understanding soft information produces the highest potential in generating accurate forecasts. Our paper portraits a future of “machine plus human” (instead of human displacement) in high-skill professions.
Keywords: AI; stock analysis; financial analysts; human-machine collaboration
JEL Codes: G11; G12; G14; G31; M41
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| AI analyst performance (C45) | performance of human analysts (D79) |
| AI analyst performance (C45) | complexity of firms (L25) |
| volume of information available (L86) | AI analyst performance (C45) |
| institutional knowledge (O36) | performance of human analysts (D79) |
| AI analyst performance (C45) | excess returns (D46) |
| alternative data access (C81) | performance of human analysts (D79) |
| AI capabilities + human insight (C45) | predictive power of AI models (C52) |