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Can generative AI help identify peer firms?

Author

Listed:
  • Yi Cao

    (George Mason University)

  • Long Chen

    (George Mason University)

  • Jennifer Wu Tucker

    (University of Florida)

  • Chi Wan

    (University of Massachusetts Boston)

Abstract

We evaluate how well generative AI can perform an important task—identifying product market competitors (“peers”). We find that machine-generated peers have a high overlap with the peers identified by human experts as well as with the peers identified by established peer identification systems. Machine-generated peers have high correlations with the focal firm in stock returns, sales growth, and gross profit margin in the subsequent year. The correlations are stronger than those derived from identifying peers by analyzing the similarity of business descriptions in annual reports or by using members in the focal firm’s SIC industry. Machine-generated peers also exhibit higher homogeneity among themselves than those identified via the two alternative systems. We demonstrate the usefulness of machine-generated peers in two settings: (1) compensation benchmarking by investors and (2) hypothesis testing by researchers. Overall, our findings suggest that generative AI can identify peer firms reasonably well, especially for large firms.

Suggested Citation

  • Yi Cao & Long Chen & Jennifer Wu Tucker & Chi Wan, 2025. "Can generative AI help identify peer firms?," Review of Accounting Studies, Springer, vol. 30(4), pages 3344-3386, December.
    • Handle: RePEc:spr:reaccs:v:30:y:2025:i:4:d:10.1007_s11142-025-09892-6
    DOI: 10.1007/s11142-025-09892-6
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