High Frequency and Unstructured Data in Finance: An Exploratory Study of Twitter
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.
William Sanger, Thierry Warin