Correlating Twitter with the stock market through non-Gaussian SVAR

Abstract

In this paper, we aim at studying the correlation between Twitter and the stock market. Specifically, we first apply non-Gaussian SVAR (structural vector autoregression) to identify possible relationships among the Twitter and stock market factors. Compared with conventional models such as Granger causality method which assume that the error items are Gaussian and only consider time-lag effect, non-Gaussian SVAR is under the assumption that the error items are non-Gaussian, better fitting the data in the stock market, and takes both instantaneous and time-lagged effects into account. We also visualize some distinctive relationships in parallel coordinates which is a well-developed multivariate visualization technique but seldom used in financial studies to the best of knowledge. Then, with the purpose of examining whether the Twitter-stock market relationship returned by non-Gaussian SVAR can help predict the stock market indicators, we build a series of regression models to predict DJI (Dow Jones Industrial Average Index) return in a sliding time window. Our experiments demonstrate that all the Twitter factors correlate with DJI return, and only the negative sentiment in tweets (posts on Twitter) is associated with DJI return volatility. Moreover, the lagged Twitter factors are more effective than the lagged stock market indicators in terms of predicting DJI return in the period of our data set.