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The relationship between twitter and stock prices. Evidence from the US technology industry

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  • Teti, Emanuele
  • Dallocchio, Maurizio
  • Aniasi, Alberto

Abstract

The widespread use of social media and the amplified interest have enormously increased the amount of data available on these platforms. This paper aims at exploring the use of social media as a tool for investing, verifying the relationship with stock prices. Rather than focusing on a market index, we analyze the technology industry in the U.S., to understand if this methodology can be used not only to capture the wider sentiment of the market, but also to invest in a single stock. OLS models are applied to verify the predictive power of Twitter and traditional media on the particular sample. The results prove that prediction markets manage to effectively pool decentralized information better alternative sources. Findings indicate higher association between the stock price of companies and high social media coverage than that with low coverage.

Suggested Citation

  • 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).
  • Handle: RePEc:eee:tefoso:v:149:y:2019:i:c:s0040162519305499
    DOI: 10.1016/j.techfore.2019.119747
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    1. Barberis, Nicholas & Thaler, Richard, 2003. "A survey of behavioral finance," Handbook of the Economics of Finance, in: G.M. Constantinides & M. Harris & R. M. Stulz (ed.), Handbook of the Economics of Finance, edition 1, volume 1, chapter 18, pages 1053-1128, Elsevier.
    2. Marianne Baxter & Robert G. King, 1999. "Measuring Business Cycles: Approximate Band-Pass Filters For Economic Time Series," The Review of Economics and Statistics, MIT Press, vol. 81(4), pages 575-593, November.
    3. 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.
    4. Bukovina, Jaroslav, 2016. "Social media big data and capital markets—An overview," Journal of Behavioral and Experimental Finance, Elsevier, vol. 11(C), pages 18-26.
    5. Ansari Saleh Ahmar & Abdul Rahman & Andi Nurani Mangkawani Arifin & Alfatih Abqary Ahmar, 2017. "Predicting movement of stock of “Y” using Sutte Indicator," Cogent Economics & Finance, Taylor & Francis Journals, vol. 5(1), pages 1347123-134, January.
    6. Siganos, Antonios & Vagenas-Nanos, Evangelos & Verwijmeren, Patrick, 2014. "Facebook's daily sentiment and international stock markets," Journal of Economic Behavior & Organization, Elsevier, vol. 107(PB), pages 730-743.
    7. Mao, Huina & Counts, Scott & Bollen, Johan, 2015. "Quantifying the effects of online bullishness on international financial markets," Statistics Paper Series 09, European Central Bank.
    8. Nofer, Michael & Hinz, Oliver, 2015. "Using Twitter to Predict the Stock Market: Where is the Mood Effect?," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 77140, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    9. Mao, Huina & Counts, Scott & Bollen, Johan, 2015. "Quantifying the effects of online bullishness on international financial markets," Statistics Paper Series 9, European Central Bank.
    10. Michael Nofer & Oliver Hinz, 2015. "Using Twitter to Predict the Stock Market," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 57(4), pages 229-242, August.
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    8. Mariano González-Sánchez & M. Encina Morales de Vega, 2021. "Influence of Bloomberg’s Investor Sentiment Index: Evidence from European Union Financial Sector," Mathematics, MDPI, vol. 9(4), pages 1-21, February.

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