Prediction from regional angst – A study of NFL sentiment in Twitter using technical stock market charting
To predict NFL game outcomes, we examine the application of technical stock market techniques to sentiment gathered from social media. From our analysis we found a $14.84 average return per sentiment-based wager compared to a $12.21 average return loss on the entire 256 games of the 2015–2016 regula...
Gespeichert in:
Veröffentlicht in: | Decision Support Systems 2017-06, Vol.98, p.80-88 |
---|---|
Hauptverfasser: | , , , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
Zusammenfassung: | To predict NFL game outcomes, we examine the application of technical stock market techniques to sentiment gathered from social media. From our analysis we found a $14.84 average return per sentiment-based wager compared to a $12.21 average return loss on the entire 256 games of the 2015–2016 regular season if using an odds-only approach. We further noted that wagers on underdogs (i.e., the less favored teams) that exhibit a “golden cross” pattern in sentiment (e.g., the most recent sentiment signal crosses the longer baseline sentiment), netted a $48.18 return per wager on 41 wagers. These results show promise of cross-domain research and we believe that applying stock market techniques to sports wagering may open an entire new research area.
•Gathered NFL tweet sentiment to predict outcomes and wagering decisions•Used difference in moving averages for optimal wagering return ($14.84/game)•Sentiment exhibiting a golden cross netted $48.18/game returns•Fans were able to signal winning teams by their change in sentiment. |
---|---|
ISSN: | 0167-9236 1873-5797 |
DOI: | 10.1016/j.dss.2017.04.010 |