Using Stock Prices as Ground Truth in Sentiment Analysis to Generate Profitable Trading Signals

Ellie Birbeck, Dave Cliff

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

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Abstract

The increasing availability of “big” (large volume) social media data has motivated a great deal of research in applying sentiment analysis to predict the movement of prices within financial markets. Previous work in this field investigates how the true sentiment of text (i.e. positive or negative opinions) can be used for financial predictions, based on the assumption that sentiments expressed online are representative of the true market sentiment. Here we consider the converse idea, that using the stock price as the ground-truth in the system may be a better indication of sentiment. Tweets are labelled as Buy or Sell dependent on whether the stock price discussed rose or fell over the following hour, and from this, stock-specific dictionaries are built for individual companies. A Bayesian classifier is used to generate stock predictions, which are input to an automated trading algorithm. Placing 468 trades over a 1 month period yields a return rate of 5.18%, which annualises to approximately 83% per annum. This approach performs significantly better than random chance and outperforms two baseline sentiment analysis methods tested.
Original languageEnglish
Title of host publication2018 IEEE Symposium Series on Computational Intelligence (SSCI 2018)
Subtitle of host publicationProceedings of a meeting held 18-21 November 2018, Bangalore, India.
EditorsSuresh Sundaram
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages1868-1875
Number of pages8
ISBN (Electronic)9781538692769
ISBN (Print)9781538692776
DOIs
Publication statusPublished - 28 Jan 2019
Event8th IEEE Symposium Series on Computational Intelligence, SSCI 2018 - Bangalore, India
Duration: 18 Nov 201821 Nov 2018

Conference

Conference8th IEEE Symposium Series on Computational Intelligence, SSCI 2018
CountryIndia
CityBangalore
Period18/11/1821/11/18

Keywords

  • Automated Trading
  • Financial Engineering
  • Financial Markets
  • Machine Learning
  • Sentiment Analysis

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    Birbeck, E., & Cliff, D. (2019). Using Stock Prices as Ground Truth in Sentiment Analysis to Generate Profitable Trading Signals. In S. Sundaram (Ed.), 2018 IEEE Symposium Series on Computational Intelligence (SSCI 2018): Proceedings of a meeting held 18-21 November 2018, Bangalore, India. (pp. 1868-1875). [8628841] Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/SSCI.2018.8628841