Can generative AI help identify peer firms?

Yi Cao, Long Chen, Jennifer Wu Tucker () and Chi Wan
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Yi Cao: George Mason University
Long Chen: George Mason University
Jennifer Wu Tucker: University of Florida
Chi Wan: University of Massachusetts Boston

Review of Accounting Studies, 2025, vol. 30, issue 4, No 6, 3344-3386

Abstract: 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.

Keywords: AI; Technology; Peers; Large language models; Machine learning; Competitors; M2; M4; G3; C8; D83; L22; O3 (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s11142-025-09892-6

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