Jim Kyung-Soo Liew (Department of Finance, Carey Business School)

Tamas Budavari (Department of Applied Mathematics and Statistics, Whiting School of Engineering)

Our area of investigation begins with attempting to understand the relationship between Twitter’s tweet sentiments by geo-location and the ability of such information to predict stock price movements and risks. An important problem that many investors face originates from not having a good understanding of the true drivers of risks associated with their stock investments. If we better understand the predictive nature of stock prices, then we could provide adequate risk management during turbulent times to insulate such investments from downside deviations. Given the increases in social media activity as evidenced by the proliferation of data generated from Twitter users, coupled with the recent evidence that links do exist between social media data and stock price movement, the natural extension would be to examine the geo-location information available on some tweets. We hypothesis that positive (negative) tweet sentiments around certain key locations will be positively (negatively) correlated with future prices movements. Some of the geo-locations that will be examined in this research include corporate headquarters and high-volume retail stores.