This study seeks to determine the possibility of a proﬁtable trading strategy when examining the relationship at an hourly level across various centrality measures. Utilizing the existing dataset from Dr. Yang, we establish a methodology to further segment the data into hourly buckets, where we also present the empirical distribution of tweets throughout the day. Predicated on the deﬁnition of the ﬁnancial community by Dr. Yang, we examine the top 2844 users’ tweets and construct a weighted sentiment measure that we can regress against hourly stock market returns. After expanding the analysis to sector ETFs of the S&P 500, we observe that the weighted sentiment measure is signiﬁcantly correlated at the lag-1 level to various ETF returns. We construct a rolling regression and back testing methodology where we observe proﬁtable trading strategies for at least one centrality measure for every ETF.
Twitter, Sentiment Analysis, Regression Analysis, Trading Strategy
Research Group (2016 Fall):
Ronak Shah, Master in Financial Engineering, Graduated in Dec 2017
Dakota Wixom, Master in Financial Engineering, Graduated in Dec 2017
Dr. Steve Yang
Overall, we see that across all indexes, at least one measure of centrality outperforms the corresponding index. With some indexes, centrality measures signiﬁcantly outperform due to multiple trading signals from the strategy. Meanwhile, other indexes have centrality measures that closely track the benchmark, which is indicative of few trading signals from the strategy. We observe a 49-55% accuracy rate of the directional prediction across all centrality measures and indexes. Given the accuracy rate, we can deﬁnitively state that trading strategies that are predicated on Twitter sentiment and that outperform the benchmark security exist.
Our study shows that sentiment analysis on Twitter messages from a ﬁnancial community, weighted by diﬀerent measures of centrality, can produce a statistically signiﬁcant predictor of the hourly returns of broad market indexes. This sentiment index can then be used to create a trading strategy that buys or sells broad market indexes based on a trained model.
Other avenues that should be considered when extending this research are position sizing and risk management. Strategies can be weighted based on historical volatility to create a risk-adjusted portfolio. In addition, a rolling sentiment indicator can be utilized to improve prediction accuracy, as there is a strong possibility that sentiment regression models should only use the immediate past and not an extensive history.