More Than Words: Quantifying Language to Measure Firms' Fundamentals

Authors

  • PAUL C. TETLOCK,

  • MAYTAL SAAR-TSECHANSKY,

  • SOFUS MACSKASSY

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    • Tetlock is with the Finance Department and Saar-Tsechansky is with the Information, Risk, and Operations Management Department at the University of Texas at Austin, McCombs School of Business. Macskassy is with Fetch Technologies. The authors are grateful for assiduous research assistance from Jie Cao and Shuming Liu. We appreciate helpful comments from Brad Barber, John Griffin, Alok Kumar, Terry Murray, David Musto, Terrance Odean, Chris Parsons, Mitchell Petersen, Laura Starks, Jeremy Stein, and Sheridan Titman, and from seminar participants at Barclays, Goldman Sachs, INSEAD, the Texas Finance Festival, University of California at Berkeley, University of Oregon, and University of Texas at Austin. We also thank two anonymous referees. Finally, we are especially grateful to the editor, Cam Harvey, and an anonymous associate editor for their excellent suggestions. The authors are responsible for any errors.


ABSTRACT

We examine whether a simple quantitative measure of language can be used to predict individual firms' accounting earnings and stock returns. Our three main findings are: (1) the fraction of negative words in firm-specific news stories forecasts low firm earnings; (2) firms' stock prices briefly underreact to the information embedded in negative words; and (3) the earnings and return predictability from negative words is largest for the stories that focus on fundamentals. Together these findings suggest that linguistic media content captures otherwise hard-to-quantify aspects of firms' fundamentals, which investors quickly incorporate into stock prices.

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