When can social media lead financial markets?

Ilya Zheludev, Robert Smith and Tomaso Aste

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

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.

JEL-codes: E6 N0 (search for similar items in EconPapers)
Date: 2014-02-27
New Economics Papers: this item is included in nep-mac
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Citations: View citations in EconPapers (21)

Published in Scientific Reports, 27, February, 2014, 4(4213). ISSN: 2045-2322

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Persistent link: https://EconPapers.repec.org/RePEc:ehl:lserod:57376

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