title: When can social media lead financial markets?
abstract: Social media analytics is showing promise for the prediction of financial markets. However, the true value of such data for trading is unclear due to a lack of consensus on which instruments can be predicted and how. Current approaches are based on the evaluation of message volumes and are typically assessed via retrospective (ex-post facto) evaluation of trading strategy returns. In this paper, we present instead a sentiment analysis methodology to quantify and statistically validate which assets could qualify for trading from social media analytics in an ex-ante configuration. We use sentiment analysis techniques and Information Theory measures to demonstrate that social media message sentiment can contain statistically-significant ex-ante information on the future prices of the S&P500 index and a limited set of stocks, in excess of what is achievable using solely message volumes.
authors.affiliation: Department of Computer Science, University College London, Gower Street, London, WC1E 6BT, UK.
authors.forename: Ilya
authors.initials: I
authors.lastname: Zheludev
authors.affiliation: Department of Computer Science, University College London, Gower Street, London, WC1E 6BT, UK.
authors.forename: Robert
authors.initials: R
authors.lastname: Smith
authors.affiliation: Department of Computer Science, University College London, Gower Street, London, WC1E 6BT, UK.
authors.forename: Tomaso
authors.initials: T
authors.lastname: Aste