Stock chatter: Using stock sentiment to predict price direction

Article type: Research Article

Authors: Rechenthin, Michael^{a; *} | Street, W. Nick^a | Srinivasan, Padmini^b

Affiliations: [a] Department of Management Science, The University of Iowa, 108 John Pappajohn Business Building, Iowa City, IA, USA, Email: [email protected] | [b] Department of Computer Science, The University of Iowa, 14 MacLean Hall, Iowa City, IA, USA

Note: [*] [email protected]

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

DOI: 10.3233/AF-13025

Journal: Algorithmic Finance, vol. 2, no. 3-4, pp. 169-196, 2013