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The Power of Micro-Blogging: How to Use Twitter for Predicting the Stock Market

Author

Listed:
  • Francesco Corea

    (LUISS Guido Carli University, Italy)

  • Enrico Maria Cervellati

    (University of Bologna, Italy)

Abstract

The availability of new data and techniques enriched the existing extensive literature on the importance of investors’ sentiment and on his impact of the stock price oscillations. The purpose of this paper is to exploit micro-blogging data in order to construct a new index-tracking variable that may be used to earn some insights on the Nasdaq-100’s future movements. The results are promising: the models augmented with the newly created variable show an incremented explanatory power with respect to the benchmark.

Suggested Citation

  • Francesco Corea & Enrico Maria Cervellati, 2015. "The Power of Micro-Blogging: How to Use Twitter for Predicting the Stock Market," Eurasian Journal of Economics and Finance, Eurasian Publications, vol. 3(4), pages 1-7.
    • Handle: RePEc:ejn:ejefjr:v:3:y:2015:i:4:p:1-7
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    References listed on IDEAS

    as
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    4. 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.
    5. Werner Antweiler & Murray Z. Frank, 2004. "Is All That Talk Just Noise? The Information Content of Internet Stock Message Boards," Journal of Finance, American Finance Association, vol. 59(3), pages 1259-1294, June.
    6. Mao, Huina & Counts, Scott & Bollen, Johan, 2015. "Quantifying the effects of online bullishness on international financial markets," Statistics Paper Series 9, European Central Bank.
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    Cited by:

    1. Katerina T. Frantzi & Konstantina Vadasi, 2017. "Multi-Word Compound Consistency Issues In Spelling. The Case Of Legal Language Of Bank Contracts," Eurasian Journal of Social Sciences, Eurasian Publications, vol. 5(1), pages 37-47.
    2. 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.
    3. Thomas Dierckx & Jesse Davis & Wim Schoutens, 2020. "Using Machine Learning and Alternative Data to Predict Movements in Market Risk," Papers 2009.07947, arXiv.org.

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