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

Abstract

Growing AI readership, proxied by expected machine downloads, motivates firms to prepare filings that are friendlier to machine parsing and processing. Firms avoid words that are perceived as negative by computational algorithms, as compared to those deemed negative only by dictionaries meant for human readers. The publication of Loughran and McDonald (2011) serves as an instrumental event attributing the difference-in-differences in the measured sentiment to machine readership. High machine-readership firms also exhibit speech emotion assessed as embodying more positivity and excitement by audio processors. This is the first study exploring the feedback effect on corporate disclosure in response to technology.

Keywords: Machine Learning, AI, Corporate Disclosure, Textual Analysis, Speech Analysis, Feedback Effect

JEL Classification: D83, G14, G30