Footnote:
In: Revised version published as: Buzzwords build momentum: Global financial Twitter sentiment and the aggregate stock market, Expert Systems with Applications, Volume 136, 2019, Pages 171-186
In: ISSN 0957-4174, DOI: 10.1016/j.eswa.2019.06.027
Nach Informationen von SSRN wurde die ursprüngliche Fassung des Dokuments October 1, 2015 erstellt
Description:
We examine the relationship between signals derived from unstructured social media microblog text data and financial market developments. Employing statistical language modeling techniques we construct directional user sentiment and non-directional topic disagreement metrics and link these to S&P 500 index returns and volatility. Based on an extensive five year sample of Twitter messages our study shows that both unsupervised and supervised statistical learning methods successfully identify subsets of expert users with distinct finance focus. This allows to filter out the substantial noise associated with social media. Accounting for salient properties of the time series in ARMA models we document significant effects of expert disagreement signals on current and future S&P volatility. Moreover, we detect a significant contemporaneous relation between expert sentiment signals and S&P returns