Making better analysts’ forecast–an investigation of artificial intelligence applications in corporations

Xiaotong Cai; Peiyang Lin; Lei Chen; Wenhua Wang

doi:10.1080/13504851.2025.2472036

Making better analysts’ forecast–an investigation of artificial intelligence applications in corporations

Xiaotong Cai
, Peiyang Lin
, Lei Chen
, Wenhua Wang^*

^*Corresponding author for this work

Management and Organisation

Research output: Contribution to Journal › Article › Academic › peer-review

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Abstract

We explore the impact of artificial intelligence (AI) applications in corporations on analysts’ earnings forecast errors. We construct firm-level AI application data using text mining methods with Chinese A-share listed companies from 2011 to 2023. Our findings reveal that AI applications in corporations significantly reduce analysts’ earnings forecasting errors. Additionally, AI applications to reduce analysts’ earnings forecast errors are achieved through improving the accounting information quality of corporations and attracting more analysts to field research activities.

Original language	English
Number of pages	7
Journal	Applied Economics Letters
DOIs	https://doi.org/10.1080/13504851.2025.2472036
Publication status	E-pub ahead of print - 26 Feb 2025

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© 2025 Informa UK Limited, trading as Taylor & Francis Group.

Keywords

accounting information quality
analysts’ earnings forecast errors
Artificial intelligence applications
field research activities

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10.1080/13504851.2025.2472036

Making better analysts forecast an investigation of artificial intelligence applications in corporationsFinal published version, 752 KBLicence: Article 25fa Dutch Copyright Act

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abstract = "We explore the impact of artificial intelligence (AI) applications in corporations on analysts{\textquoteright} earnings forecast errors. We construct firm-level AI application data using text mining methods with Chinese A-share listed companies from 2011 to 2023. Our findings reveal that AI applications in corporations significantly reduce analysts{\textquoteright} earnings forecasting errors. Additionally, AI applications to reduce analysts{\textquoteright} earnings forecast errors are achieved through improving the accounting information quality of corporations and attracting more analysts to field research activities.",

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Making better analysts’ forecast–an investigation of artificial intelligence applications in corporations

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Keywords

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