Why we twitter: understanding microblogging usage and communities
Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 workshop on Web mining and social network analysis
Proceedings of the first workshop on Online social networks
Earthquake shakes Twitter users: real-time event detection by social sensors
Proceedings of the 19th international conference on World wide web
Predicting the Future with Social Media
WI-IAT '10 Proceedings of the 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
Correlating financial time series with micro-blogging activity
Proceedings of the fifth ACM international conference on Web search and data mining
Correlating S&P 500 stocks with Twitter data
Proceedings of the First ACM International Workshop on Hot Topics on Interdisciplinary Social Networks Research
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Stock is a popular topic in Twitter. The number of tweets concerning a stock varies over days, and sometimes exhibits a significant spike. In this paper, we investigate Twitter volume spikes related to S&P 500 stocks, and whether they are useful for stock trading. Through correlation analysis, we provide insight on when Twitter volume spikes occur and possible causes of these spikes. We further explore whether these spikes are surprises to market participants by comparing the implied volatility of a stock before and after a Twitter volume spike. Moreover, we develop a Bayesian classifier that uses Twitter volume spikes to assist stock trading, and show that it can provide substantial profit. We further develop an enhanced strategy that combines the Bayesian classifier and a stock bottom picking method, and demonstrate that it can achieve significant gain in a short amount of time. Simulation over a half year's stock market data indicates that it achieves on average 8.6% gain in 27 trading days and 15.0% gain in 55 trading days. Statistical tests show that the gain is statistically significant, and the enhanced strategy significantly outperforms the strategy that only uses the Bayesian classifier as well as a bottom picking method that uses trading volume spikes.