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

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  • Ellie Birbeck
  • Dave Cliff

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.

Suggested Citation

  • Ellie Birbeck & Dave Cliff, 2018. "Using Stock Prices as Ground Truth in Sentiment Analysis to Generate Profitable Trading Signals," Papers 1811.02886, arXiv.org.
  • Handle: RePEc:arx:papers:1811.02886
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    References listed on IDEAS

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    1. Timm O. Sprenger & Andranik Tumasjan & Philipp G. Sandner & Isabell M. Welpe, 2014. "Tweets and Trades: the Information Content of Stock Microblogs," European Financial Management, European Financial Management Association, vol. 20(5), pages 926-957, November.
    2. Harrison Hong & Jeremy C. Stein, 1999. "A Unified Theory of Underreaction, Momentum Trading, and Overreaction in Asset Markets," Journal of Finance, American Finance Association, vol. 54(6), pages 2143-2184, December.
    3. Ying Zhang & Peggy Swanson, 2010. "Are day traders bias free?—evidence from internet stock message boards," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 34(1), pages 96-112, January.
    4. Timmermann, Allan, 2008. "Reply to the discussion of Elusive Return Predictability," International Journal of Forecasting, Elsevier, vol. 24(1), pages 29-30.
    5. Tim Loughran & Bill Mcdonald, 2011. "When Is a Liability Not a Liability? Textual Analysis, Dictionaries, and 10‐Ks," Journal of Finance, American Finance Association, vol. 66(1), pages 35-65, February.
    6. Timmermann, Allan, 2008. "Elusive return predictability," International Journal of Forecasting, Elsevier, vol. 24(1), pages 1-18.
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    Cited by:

    1. Dave Cliff, 2021. "BBE: Simulating the Microstructural Dynamics of an In-Play Betting Exchange via Agent-Based Modelling," Papers 2105.08310, arXiv.org.
    2. Irina RAICU, 2019. "Financial Banking Dataset for Supervised Machine Learning Classification," Informatica Economica, Academy of Economic Studies - Bucharest, Romania, vol. 23(1), pages 37-49.
    3. Hung, Ming-Chin & Hsia, Ping-Hung & Kuang, Xian-Ji & Lin, Shih-Kuei, 2024. "Intelligent portfolio construction via news sentiment analysis," International Review of Economics & Finance, Elsevier, vol. 89(PA), pages 605-617.

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