Stock chatter: Using stock sentiment to predict price direction

Michael Rechenthin (), W. Nick Street and Padmini Srinivasan
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Michael Rechenthin: Department of Management Science, The University of Iowa, Postal: Department of Management Science, The University of Iowa, 108 John Pappajohn Business Building,, Iowa City, IA, USA
W. Nick Street: Department of Management Science, The University of Iowa, Postal: Department of Management Science, The University of Iowa, 108 John Pappajohn Business Building,, Iowa City, IA, USA
Padmini Srinivasan: Department of Computer Science, The University of Iowa, Postal: Department of Computer Science, The University of Iowa, 14 MacLean Hall, Iowa City, IA, USA

Algorithmic Finance, 2013, vol. 2, issue 3-4, 169-196

Abstract: This paper examines a popular stock message board and finds slight daily predictability using supervised learning algorithms when combining daily sentiment with historical price information. Additionally, with the profit potential in trading stocks, it is of no surprise that a number of popular financial websites are attempting to capture investor sentiment by providing an aggregate of this negative and positive online emotion. We question if the existence of dishonest posters are capitalizing on the popularity of the boards by writing sentiment in line with their trading goals as a means of influencing others, and therefore undermining the purpose of the boards. We exclude these posters to determine if predictability increases, but find no discernible difference.

Keywords: prediction; classification; sentiment analysis; stock; equity; tweet; Yahoo finance; message boards (search for similar items in EconPapers)
JEL-codes: C10 D40 (search for similar items in EconPapers)
Date: 2013
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Citations: View citations in EconPapers (10)

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Persistent link: https://EconPapers.repec.org/RePEc:ris:iosalg:0012

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