Journal
SCIENTIFIC REPORTS
Volume 4, Issue -, Pages -Publisher
NATURE PUBLISHING GROUP
DOI: 10.1038/srep04213
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Funding
- Engineering and Physical Sciences Research Council of the United Kingdom
- Economic and Social Research Council [ES/K002309/1] Funding Source: researchfish
- ESRC [ES/K002309/1] Funding Source: UKRI
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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.
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