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

Sean Cao, Wei Jiang, Baozhong Yang and Alan L. Zhang

No 27950, NBER Working Papers from National Bureau of Economic Research, Inc

Abstract: This paper analyzes how corporate disclosure has been reshaped by machine processors, employed by algorithmic traders, robot investment advisors, and quantitative analysts. Our findings indicate that increasing machine and AI readership, proxied by machine downloads, motivates firms to prepare filings that are more friendly to machine parsing and processing. Moreover, firms with high expected machine downloads manage textual sentiment and audio emotion in ways catered to machine and AI readers, such as by differentially avoiding words that are perceived as negative by computational algorithms as compared to those by human readers, and by exhibiting speech emotion favored by machine learning software processors. The publication of Loughran and McDonald (2011) is instrumental in attributing the change in the measured sentiment to machine and AI readership. While existing research has explored how investors and researchers apply machine learning and computational tools to quantify qualitative information from disclosure and news, this study is the first to identify and analyze the feedback effect on corporate disclosure decisions, i.e., how companies adjust the way they talk knowing that machines are listening.

JEL-codes: G14 G30 (search for similar items in EconPapers)
Date: 2020-10
New Economics Papers: this item is included in nep-big and nep-cmp
Note: AP CF LE
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