How to Talk When a Machine Is Listening: Corporate Disclosure in the Age of AI

• 成果类型: Article
• 署名作者: Cao, Sean; Jiang, Wei; Yang, Baozhong; Zhang, Alan L.
• 署名单位: University System of Maryland; University of Maryland College Park; Emory University; University System of Georgia; Georgia State University; State University System of Florida; Florida International University
• 刊物名称: REVIEW OF FINANCIAL STUDIES
• ISSN/ISSBN: 0893-9454
• DOI: 10.1093/rfs/hhad021
• 发表日期: 2023
• 页码: 3603
• 关键词: institutional investors; Textual analysis; INFORMATION; liquidity; Sentiment; search; cost; language

摘要

Growing AI readership (proxied for by machine downloads and ownership by AI-equipped investors) motivates firms to prepare filings friendlier to machine processing and to mitigate linguistic tones that are unfavorably perceived by algorithms. Loughran and McDonald (2011) and BERT available since 2018 serve as event studies supporting attribution of the decrease in the measured negative sentiment to increased machine readership. This relationship is stronger among firms with higher benefits to (e.g., external financing needs) or lower cost (e.g., litigation risk) of sentiment management. This is the first study exploring the feedback effect on corporate disclosure in response to technology.