29 Pages Posted: 18 Jan 2014
Date Written: 2013
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.
Suggested Citation: Suggested Citation
Rechenthin, Michael and Street, W. Nick and Srinivasan, Padmini, Stock Chatter: Using Stock Sentiment to Predict Price Direction (2013). Algorithmic Finance 2013, 2:3-4, 169-196. Available at SSRN: https://ssrn.com/abstract=2380419
By Andrew Ang
By Todd Feldman