Zheludev, I;
Smith, R;
Aste, T;
(2014)
When Can Social Media Lead Financial Markets?
Scientific Reports
, 4
, Article 4213. 10.1038/srep04213.
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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.
| Type: | Article |
|---|---|
| Title: | When Can Social Media Lead Financial Markets? |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1038/srep04213 |
| Publisher version: | http://dx.doi.org/10.1038/srep04213 |
| Language: | English |
| Additional information: | This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-nd/3.0/. / Not to be confused with Dr Zheludev's thesis with the same title. |
| Keywords: | Computational science, Information theory and computation, Statistics |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/1430614 |
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