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The impact of Twitter sentiment on renewable energy stocks

Author

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  • Reboredo, Juan C.
  • Ugolini, Andrea

Abstract

We study the impact of Twitter sentiment and sentiment divergence on returns, volatility and trading volumes for renewable energy stocks. Based on daily time series for Twitter sentiment and Twitter sentiment divergence, we estimate VAR models and evaluate spillovers between sentiment and renewable energy stock pricing and trading. We find that whereas Twitter sentiment has no sizeable impact on returns, volatility or trading volumes, Twitter sentiment divergence generates feedback effects on volatility and trading volumes. Our evidence would indicate that the wisdom of the Twitter crowd is not substantial in shaping prices and trading for renewable energy companies.

Suggested Citation

  • Reboredo, Juan C. & Ugolini, Andrea, 2018. "The impact of Twitter sentiment on renewable energy stocks," Energy Economics, Elsevier, vol. 76(C), pages 153-169.
  • Handle: RePEc:eee:eneeco:v:76:y:2018:i:c:p:153-169
    DOI: 10.1016/j.eneco.2018.10.014
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    References listed on IDEAS

    as
    1. Diebold, Francis X. & Yılmaz, Kamil, 2014. "On the network topology of variance decompositions: Measuring the connectedness of financial firms," Journal of Econometrics, Elsevier, vol. 182(1), pages 119-134.
    2. Guo, Jian-Feng & Ji, Qiang, 2013. "How does market concern derived from the Internet affect oil prices?," Applied Energy, Elsevier, vol. 112(C), pages 1536-1543.
    3. Malcolm Baker & Jeffrey Wurgler, 2007. "Investor Sentiment in the Stock Market," Journal of Economic Perspectives, American Economic Association, vol. 21(2), pages 129-152, Spring.
    4. Annette Meinusch & Peter Tillmann, 2017. "Quantitative Easing and Tapering Uncertainty: Evidence from Twitter," International Journal of Central Banking, International Journal of Central Banking, vol. 13(4), pages 227-258, December.
    5. Kaplanski, Guy & Levy, Haim & Veld, Chris & Veld-Merkoulova, Yulia, 2015. "Do Happy People Make Optimistic Investors?," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 50(1-2), pages 145-168, April.
    6. Pesaran, H. Hashem & Shin, Yongcheol, 1998. "Generalized impulse response analysis in linear multivariate models," Economics Letters, Elsevier, vol. 58(1), pages 17-29, January.
    7. Jaroslav Bukovina, 2016. "Social Media and Capital Markets – an Overview," MENDELU Working Papers in Business and Economics 2016-57, Mendel University in Brno, Faculty of Business and Economics.
    8. Michael H. Breitner & Christian Dunis & Hans-Jörg Mettenheim & Christopher Neely & Georgios Sermpinis & Azizah Abu Bakar & Antonios Siganos & Evangelos Vagenas‐Nanos, 2014. "Does Mood Explain the Monday Effect?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 33(6), pages 409-418, September.
    9. Han, Liyan & Lv, Qiuna & Yin, Libo, 2017. "Can investor attention predict oil prices?," Energy Economics, Elsevier, vol. 66(C), pages 547-558.
    10. Casey Dougal & Joseph Engelberg & Diego García & Christopher A. Parsons, 2012. "Journalists and the Stock Market," Review of Financial Studies, Society for Financial Studies, vol. 25(3), pages 639-679.
    11. 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.
    12. Garman, Mark B & Klass, Michael J, 1980. "On the Estimation of Security Price Volatilities from Historical Data," The Journal of Business, University of Chicago Press, vol. 53(1), pages 67-78, January.
    13. Siganos, Antonios & Vagenas-Nanos, Evangelos & Verwijmeren, Patrick, 2017. "Divergence of sentiment and stock market trading," Journal of Banking & Finance, Elsevier, vol. 78(C), pages 130-141.
    14. Vahid Gholampour & Eric van Wincoop, 2017. "What can we Learn from Euro-Dollar Tweets?," NBER Working Papers 23293, National Bureau of Economic Research, Inc.
    15. Reboredo, Juan C. & Rivera-Castro, Miguel A. & Ugolini, Andrea, 2017. "Wavelet-based test of co-movement and causality between oil and renewable energy stock prices," Energy Economics, Elsevier, vol. 61(C), pages 241-252.
    16. Reboredo, Juan C., 2015. "Is there dependence and systemic risk between oil and renewable energy stock prices?," Energy Economics, Elsevier, vol. 48(C), pages 32-45.
    17. Snehal Banerjee & Ilan Kremer, 2010. "Disagreement and Learning: Dynamic Patterns of Trade," Journal of Finance, American Finance Association, vol. 65(4), pages 1269-1302, August.
    18. Diebold, Francis X. & Yilmaz, Kamil, 2012. "Better to give than to receive: Predictive directional measurement of volatility spillovers," International Journal of Forecasting, Elsevier, vol. 28(1), pages 57-66.
    19. Harrison Hong & Jeremy C. Stein, 2007. "Disagreement and the Stock Market," Journal of Economic Perspectives, American Economic Association, vol. 21(2), pages 109-128, Spring.
    20. Tim Bollerslev & Jia Li & Yuan Xue, 2018. "Volume, Volatility, and Public News Announcements," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 85(4), pages 2005-2041.
    21. 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.
    22. Russell Jame & Rick Johnston & Stanimir Markov & Michael C. Wolfe, 2016. "The Value of Crowdsourced Earnings Forecasts," Journal of Accounting Research, Wiley Blackwell, vol. 54(4), pages 1077-1110, September.
    23. 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).
    24. repec:men:wpaper:57_2015 is not listed on IDEAS
    25. Hailiang Chen & Prabuddha De & Yu (Jeffrey) Hu & Byoung-Hyoun Hwang, 2014. "Wisdom of Crowds: The Value of Stock Opinions Transmitted Through Social Media," Review of Financial Studies, Society for Financial Studies, vol. 27(5), pages 1367-1403.
    26. 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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    More about this item

    Keywords

    Twitter; Social media; Sentiment; Renewable energy; Stock market;
    All these keywords.

    JEL classification:

    • C3 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables
    • G1 - Financial Economics - - General Financial Markets
    • Q4 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy

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