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

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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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    Cited by:

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    8. Joseph D. Prusa & Ryan T. Sagul & Taghi M. Khoshgoftaar, 2019. "Extracting Knowledge from Technical Reports for the Valuation of West Texas Intermediate Crude Oil Futures," Information Systems Frontiers, Springer, vol. 21(1), pages 109-123, February.
    9. Heba Ali, 2018. "Twitter, Investor Sentiment and Capital Markets: What Do We Know?," International Journal of Economics and Finance, Canadian Center of Science and Education, vol. 10(8), pages 158-158, August.
    10. Afees A. Salisu & Raymond Swaray & Tirimisyu F. Oloko, 2017. "A multi-factor predictive model for oil-US stock nexus with persistence, endogeneity and conditional heteroscedasticity effects," Working Papers 024, Centre for Econometric and Allied Research, University of Ibadan.
    11. Shen, Dehua & Urquhart, Andrew & Wang, Pengfei, 2019. "Does twitter predict Bitcoin?," Economics Letters, Elsevier, vol. 174(C), pages 118-122.
    12. Shilpa Srivastava & Millie Pant & Varuna Gupta, 2023. "Analysis and prediction of Indian stock market: a machine-learning approach," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 14(4), pages 1567-1585, August.
    13. Na, Haejung & Kim, Soonho, 2021. "Predicting stock prices based on informed traders’ activities using deep neural networks," Economics Letters, Elsevier, vol. 204(C).
    14. Kumar, Rahul & Deb, Soumya Guha & Mukherjee, Shubhadeep, 2020. "Do words reveal the latent truth? Identifying communication patterns of corporate losers," Journal of Behavioral and Experimental Finance, Elsevier, vol. 26(C).
    15. Wang, Fang & Gacesa, Marko, 2023. "Semi-strong efficient market of Bitcoin and Twitter: An analysis of semantic vector spaces of extracted keywords and light gradient boosting machine models," International Review of Financial Analysis, Elsevier, vol. 88(C).
    16. U, JuHyok & Lu, PengYu & Kim, ChungSong & Ryu, UnSok & Pak, KyongSok, 2020. "A new LSTM based reversal point prediction method using upward/downward reversal point feature sets," Chaos, Solitons & Fractals, Elsevier, vol. 132(C).
    17. Qiong Wu & Christopher G. Brinton & Zheng Zhang & Andrea Pizzoferrato & Zhenming Liu & Mihai Cucuringu, 2019. "Equity2Vec: End-to-end Deep Learning Framework for Cross-sectional Asset Pricing," Papers 1909.04497, arXiv.org, revised Oct 2021.
    18. Mohammad Alomari & Abdel Razzaq Al rababa’a & Ghaith El-Nader & Ahmad Alkhataybeh, 2021. "Who’s behind the wheel? The role of social and media news in driving the stock–bond correlation," Review of Quantitative Finance and Accounting, Springer, vol. 57(3), pages 959-1007, October.
    19. Ning Wang & Shanhui Ke & Yibo Chen & Tao Yan & Andrew Lim, 2019. "Textual Sentiment of Chinese Microblog Toward the Stock Market," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 18(02), pages 649-671, March.
    20. Andrea Fronzetti Colladon & Stefano Grassi & Francesco Ravazzolo & Francesco Violante, 2023. "Forecasting financial markets with semantic network analysis in the COVID‐19 crisis," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(5), pages 1187-1204, August.
    21. Teti, Emanuele & Dallocchio, Maurizio & Aniasi, Alberto, 2019. "The relationship between twitter and stock prices. Evidence from the US technology industry," Technological Forecasting and Social Change, Elsevier, vol. 149(C).
    22. Naderi Semiromi, Hamed & Lessmann, Stefan & Peters, Wiebke, 2020. "News will tell: Forecasting foreign exchange rates based on news story events in the economy calendar," The North American Journal of Economics and Finance, Elsevier, vol. 52(C).
    23. Toan Luu Duc Huynh, 2023. "When Elon Musk Changes his Tone, Does Bitcoin Adjust Its Tune?," Computational Economics, Springer;Society for Computational Economics, vol. 62(2), pages 639-661, August.

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