alexa Abstract | High Frequency and Unstructured Data in Finance: An Exploratory Study of Twitter
ISSN: 1948-1432

Journal of Global Research in Computer Sciences
Open Access

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Research Article Open Access

Abstract

Objective: In this paper, we investigate the question to know whether information spread over Twitter can be useful to design investment strategies on financial markets.

Methods: We compare the influence of two kinds of messages sent on Twitter over two types of returns concerning firms listed on the S&P500. We use logistic-based models to assess the probability of having certain types of returns based on messages published on Twitter.

Results: Financial tweets are positively correlated with higher intraday and overnight returns (1 to 5% returns) while being negatively correlated with lower returns (0 to 1% returns). Non-financial tweets are not significantly related to such returns.

Conclusion: From a practical standpoint, investment strategies could be designed following these findings to optimize some gain opportunities depending on the investment day, the targeted industry and live activity on Twitter.

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Author(s): William Sanger, Thierry Warin

Keywords

Social media, Stock prices, Big data, S&P500, Twitter, High frequency, Unstructured data, Computer Science, Computer Engineering

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