IDEAS home Printed from https://ideas.repec.org/a/ejn/ejefjr/v3y2015i4p1-7.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://eurasianpublications.com/wp-content/uploads/2021/02/EJEF-3.4.1.-Corea-and-Cervellati-pp.1-7.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Huina Mao & Scott Counts & Johan Bollen, 2011. "Predicting Financial Markets: Comparing Survey, News, Twitter and Search Engine Data," Papers 1112.1051, arXiv.org.
    2. 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.
    3. Mao, Huina & Counts, Scott & Bollen, Johan, 2015. "Quantifying the effects of online bullishness on international financial markets," Statistics Paper Series 09, European Central Bank.
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Gu, Chen & Kurov, Alexander, 2020. "Informational role of social media: Evidence from Twitter sentiment," Journal of Banking & Finance, Elsevier, vol. 121(C).
    2. Mark Johnman & Bruce James Vanstone & Adrian Gepp, 2018. "Predicting FTSE 100 returns and volatility using sentiment analysis," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 58(S1), pages 253-274, November.
    3. Eric. W. K. See-To & Yang Yang, 2017. "Market sentiment dispersion and its effects on stock return and volatility," Electronic Markets, Springer;IIM University of St. Gallen, vol. 27(3), pages 283-296, August.
    4. Gholampour, Vahid & van Wincoop, Eric, 2019. "Exchange rate disconnect and private information: What can we learn from Euro-Dollar tweets?," Journal of International Economics, Elsevier, vol. 119(C), pages 111-132.
    5. Vahid Gholampour & Eric van Wincoop, 2017. "What can we Learn from Euro-Dollar Tweets?," NBER Working Papers 23293, National Bureau of Economic Research, Inc.
    6. Al-Nasseri, Alya & Menla Ali, Faek, 2018. "What does investors' online divergence of opinion tell us about stock returns and trading volume?," Journal of Business Research, Elsevier, vol. 86(C), pages 166-178.
    7. Andrew Todd & James Bowden & Yashar Moshfeghi, 2024. "Text‐based sentiment analysis in finance: Synthesising the existing literature and exploring future directions," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 31(1), March.
    8. Gholampour, Vahid, 2019. "Daily expectations of returns index," Journal of Empirical Finance, Elsevier, vol. 54(C), pages 236-252.
    9. Caporale, Guglielmo Maria & Spagnolo, Fabio & Spagnolo, Nicola, 2016. "Macro news and stock returns in the Euro area: A VAR-GARCH-in-mean analysis," International Review of Financial Analysis, Elsevier, vol. 45(C), pages 180-188.
    10. Ferdinand Graf, 2011. "Mechanically Extracted Company Signals and their Impact on Stock and Credit Markets," Working Paper Series of the Department of Economics, University of Konstanz 2011-18, Department of Economics, University of Konstanz.
    11. Thomas Renault, 2020. "Sentiment analysis and machine learning in finance: a comparison of methods and models on one million messages," Digital Finance, Springer, vol. 2(1), pages 1-13, September.
    12. Steven Heston & Nitish R. Sinha, 2016. "News versus Sentiment : Predicting Stock Returns from News Stories," Finance and Economics Discussion Series 2016-048, Board of Governors of the Federal Reserve System (U.S.).
    13. 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.
    14. Gabriele Ranco & Darko Aleksovski & Guido Caldarelli & Miha Grčar & Igor Mozetič, 2015. "The Effects of Twitter Sentiment on Stock Price Returns," PLOS ONE, Public Library of Science, vol. 10(9), pages 1-21, September.
    15. Matteo Accornero & Mirko Moscatelli, 2018. "Listening to the buzz: social media sentiment and retail depositors' trust," Temi di discussione (Economic working papers) 1165, Bank of Italy, Economic Research and International Relations Area.
    16. Xin Li & Kun Chen & Sherry X. Sun & Terrance Fung & Huaiqing Wang & Daniel D. Zeng, 2016. "A Commonsense Knowledge-Enabled Textual Analysis Approach for Financial Market Surveillance," INFORMS Journal on Computing, INFORMS, vol. 28(2), pages 278-294, May.
    17. Groß-Klußmann, Axel & Hautsch, Nikolaus, 2011. "When machines read the news: Using automated text analytics to quantify high frequency news-implied market reactions," Journal of Empirical Finance, Elsevier, vol. 18(2), pages 321-340, March.
    18. Gabriele Ranco & Ilaria Bordino & Giacomo Bormetti & Guido Caldarelli & Fabrizio Lillo & Michele Treccani, 2014. "Coupling news sentiment with web browsing data improves prediction of intra-day price dynamics," Papers 1412.3948, arXiv.org, revised Dec 2015.
    19. Vegard Høghaug Larsen & Leif Anders Thorsrud, 2022. "Asset returns, news topics, and media effects," Scandinavian Journal of Economics, Wiley Blackwell, vol. 124(3), pages 838-868, July.
    20. Miwa, Kotaro, 2023. "Divergent opinions on social media," International Review of Economics & Finance, Elsevier, vol. 86(C), pages 182-196.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ejn:ejefjr:v:3:y:2015:i:4:p:1-7. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Esra Barakli (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.