Divulgação Financeira na Era da Inteligência Artificial

In How to Talk When a Machine Is Listening: Corporate Disclosure in the Age of AI the authors explore some of the implications of this trend. Rather than focusing on how investors and researchers apply machine learning to extract information, this study examines how companies adjust their language and reporting in order to achieve maximum impact with algorithms that are processing corporate disclosures.

To gauge the extent of a company’s expected machine readership, the researchers use a proxy: the number of machine downloads of the company’s filings from the US Securities and Exchange Commission’s electronic retrieval system. Mechanical downloads of corporate 10-K and 10-Q filings have increased exponentially, from 360,861 in 2003 to around 165 million in 2016. Machine downloads have become the dominant mode during this time — increasing from 39 percent of all downloads in 2003 to 78 percent in 2016.

The researchers find that companies expecting higher levels of machine readership prepare their disclosures in ways that are more readable by this audience. “Machine readability” is measured in terms of how easily the information can be processed and parsed, with a one standard deviation increase in expected machine downloads corresponding to a 0.24 standard deviation increase in machine readability. For example, a table in a disclosure document might receive a low readability score because its formatting makes it difficult for a machine to recognize it as a table. A table in a disclosure document would receive a high readability score if it made effective use of tagging so that a machine could easily identify and analyze the content.

Companies also go beyond machine readability and manage the sentiment and tone of their disclosures to induce algorithmic readers to draw favorable conclusions about the content. For example, companies avoid words that are listed as negative in the directions given to algorithms.

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Both results demonstrate that company managers specifically consider machine readers, as well as humans, when preparing disclosures.

Machines have become an important part of the audience not just for written documents but also for earnings calls and other conversations with investors. Managers who know that their disclosure documents are being parsed by machines may also recognize that voice analyzers may be used to identify vocal patterns and emotions in their commentary. Using machine learning software trained on a sample of conference call audio from 2010 to 2016, the researchers show that the vocal tones of managers at companies with higher expected machine readership are measurably more positive and excited.

How to Talk When a Machine is Listening: Corporate Disclosure in the Age of AISean Cao, Wei Jiang, Baozhong Yang, and Alan L. ZhangNBER Working Paper No. 27950October 2020