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Publication - Professor Dave Cliff

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

    Citation

    Birbeck, E & Cliff, D, 2019, ‘Using Stock Prices as Ground Truth in Sentiment Analysis to Generate Profitable Trading Signals’. in: Suresh Sundaram (eds) 2018 IEEE Symposium Series on Computational Intelligence (SSCI 2018): Proceedings of a meeting held 18-21 November 2018, Bangalore, India.. Institute of Electrical and Electronics Engineers (IEEE), pp. 1868-1875

    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.

    Full details in the University publications repository