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Trade the tweet: Social media text mining and sparse matrix factorization for stock market prediction

Author

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  • Sun, Andrew
  • Lachanski, Michael
  • Fabozzi, Frank J.

Abstract

We investigate the potential use of textual information from user-generated microblogs to predict the stock market. Utilizing the latent space model proposed by Wong et al. (2014), we correlate the movements of both stock prices and social media content. This study differs from models in prior studies in two significant ways: (1) it leverages market information contained in high-volume social media data rather than news articles and (2) it does not evaluate sentiment. We test this model on data spanning from 2011 to 2015 on a majority of stocks listed in the S&P 500 Index and find that our model outperforms a baseline regression. We conclude by providing a trading strategy that produces an attractive annual return and Sharpe ratio.

Suggested Citation

  • Sun, Andrew & Lachanski, Michael & Fabozzi, Frank J., 2016. "Trade the tweet: Social media text mining and sparse matrix factorization for stock market prediction," International Review of Financial Analysis, Elsevier, vol. 48(C), pages 272-281.
    • Handle: RePEc:eee:finana:v:48:y:2016:i:c:p:272-281
    DOI: 10.1016/j.irfa.2016.10.009
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    References listed on IDEAS

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    1. Paul C. Tetlock & Maytal Saar‐Tsechansky & Sofus Macskassy, 2008. "More Than Words: Quantifying Language to Measure Firms' Fundamentals," Journal of Finance, American Finance Association, vol. 63(3), pages 1437-1467, June.
    2. Paul C. Tetlock, 2007. "Giving Content to Investor Sentiment: The Role of Media in the Stock Market," Journal of Finance, American Finance Association, vol. 62(3), pages 1139-1168, June.
    3. repec:bla:jfinan:v:59:y:2004:i:3:p:1259-1294 is not listed on IDEAS
    4. Fama, Eugene F, 1970. "Efficient Capital Markets: A Review of Theory and Empirical Work," Journal of Finance, American Finance Association, vol. 25(2), pages 383-417, May.
    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. Felix Ming Fai Wong & Zhenming Liu & Mung Chiang, 2014. "Stock Market Prediction from WSJ: Text Mining via Sparse Matrix Factorization," Papers 1406.7330, arXiv.org.
    Full references (including those not matched with items on IDEAS)

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