IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/114155.html
   My bibliography  Save this paper

Do Twitter Sentiments Really Effective on Energy Stocks? Evidence from Intercompany Dependency

Author

Listed:
  • Yılmaz, Emrah Sıtkı
  • Ozpolat, Aslı
  • Destek, Mehmet Akif

Abstract

The study aims to examine the effects of social media activities on stock prices of the energy sector. In this respect, the sample covers the monthly period from 2015m6 to 2020m5 has been observed. Energy stocks as S&P 500 index (SP), stock market volatility index (VIX), trade-weighted USD index (USD) and Brent oil prices (OIL) have been used as independent variables. Accordingly, three different models have been created to analyze the link between returns, volatility and trading volume and Twitter sentiments by using Augment mean Group. As a result, we found that Twitter sentiment values have no significant impact on the returns and volatility of the companies. Tweets, on the other hand, appear to have a favorable impact on company trading volume values.

Suggested Citation

  • Yılmaz, Emrah Sıtkı & Ozpolat, Aslı & Destek, Mehmet Akif, 2022. "Do Twitter Sentiments Really Effective on Energy Stocks? Evidence from Intercompany Dependency," MPRA Paper 114155, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:114155
    as

    Download full text from publisher

    File URL: https://mpra.ub.uni-muenchen.de/114155/1/MPRA_paper_114155.pdf
    File Function: original version
    Download Restriction: no
    ---><---

    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. David Hirshleifer & Tyler Shumway, 2003. "Good Day Sunshine: Stock Returns and the Weather," Journal of Finance, American Finance Association, vol. 58(3), pages 1009-1032, June.
    3. Jin, Li & Myers, Stewart C., 2006. "R2 around the world: New theory and new tests," Journal of Financial Economics, Elsevier, vol. 79(2), pages 257-292, February.
    4. Majumdar, Adrija & Bose, Indranil, 2019. "Do tweets create value? A multi-period analysis of Twitter use and content of tweets for manufacturing firms," International Journal of Production Economics, Elsevier, vol. 216(C), pages 1-11.
    5. Zhi Da & Joseph Engelberg & Pengjie Gao, 2015. "Editor's Choice The Sum of All FEARS Investor Sentiment and Asset Prices," Review of Financial Studies, Society for Financial Studies, vol. 28(1), pages 1-32.
    6. M. Hashem Pesaran, 2021. "General diagnostic tests for cross-sectional dependence in panels," Empirical Economics, Springer, vol. 60(1), pages 13-50, January.
    7. 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.
    8. Milla Siikanen & Kk{e}stutis Baltakys & Juho Kanniainen & Ravi Vatrapu & Raghava Mukkamala & Abid Hussain, 2017. "Facebook drives behavior of passive households in stock markets," Papers 1709.07300, arXiv.org, revised May 2018.
    9. 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.
    10. You, Wanhai & Guo, Yawei & Peng, Cheng, 2017. "Twitter's daily happiness sentiment and the predictability of stock returns," Finance Research Letters, Elsevier, vol. 23(C), pages 58-64.
    11. 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.
    12. Gregory S. Miller, 2006. "The Press as a Watchdog for Accounting Fraud," Journal of Accounting Research, Wiley Blackwell, vol. 44(5), pages 1001-1033, December.
    13. 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.
    14. Han, Liyan & Lv, Qiuna & Yin, Libo, 2017. "Can investor attention predict oil prices?," Energy Economics, Elsevier, vol. 66(C), pages 547-558.
    15. Lily Fang & Joel Peress, 2009. "Media Coverage and the Cross‐section of Stock Returns," Journal of Finance, American Finance Association, vol. 64(5), pages 2023-2052, October.
    16. Reboredo, Juan C. & Ugolini, Andrea, 2018. "The impact of Twitter sentiment on renewable energy stocks," Energy Economics, Elsevier, vol. 76(C), pages 153-169.
    17. Zhang, Wei & Wang, Pengfei & Li, Xiao & Shen, Dehua, 2018. "Twitter’s daily happiness sentiment and international stock returns: Evidence from linear and nonlinear causality tests," Journal of Behavioral and Experimental Finance, Elsevier, vol. 18(C), pages 50-53.
    18. 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.
    19. Zu, Xu & Diao, Xinyi & Meng, Zhiyi, 2019. "The impact of social media input intensity on firm performance: Evidence from Sina Weibo," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 536(C).
    20. Aman, Hiroyuki, 2013. "An analysis of the impact of media coverage on stock price crashes and jumps: Evidence from Japan," Pacific-Basin Finance Journal, Elsevier, vol. 24(C), pages 22-38.
    21. Wang, Zhan & Kim, Hyun Gon, 2017. "Can Social Media Marketing Improve Customer Relationship Capabilities and Firm Performance? Dynamic Capability Perspective," Journal of Interactive Marketing, Elsevier, vol. 39(C), pages 15-26.
    22. Zhang, Tonghui & Yuan, Ying & Wu, Xi, 2020. "Is microblogging data reflected in stock market volatility? Evidence from Sina Weibo," Finance Research Letters, Elsevier, vol. 32(C).
    23. Saunders, Edward M, Jr, 1993. "Stock Prices and Wall Street Weather," American Economic Review, American Economic Association, vol. 83(5), pages 1337-1345, December.
    24. T. S. Breusch & A. R. Pagan, 1980. "The Lagrange Multiplier Test and its Applications to Model Specification in Econometrics," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 47(1), pages 239-253.
    25. Kaplanski, Guy & Levy, Haim, 2010. "Sentiment and stock prices: The case of aviation disasters," Journal of Financial Economics, Elsevier, vol. 95(2), pages 174-201, February.
    26. Afkhami, Mohamad & Cormack, Lindsey & Ghoddusi, Hamed, 2017. "Google search keywords that best predict energy price volatility," Energy Economics, Elsevier, vol. 67(C), pages 17-27.
    27. 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.
    28. Timm O. Sprenger & Andranik Tumasjan & Philipp G. Sandner & Isabell M. Welpe, 2014. "Tweets and Trades: the Information Content of Stock Microblogs," European Financial Management, European Financial Management Association, vol. 20(5), pages 926-957, November.
    29. 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.
    30. Li, Xiao & Shen, Dehua & Xue, Mei & Zhang, Wei, 2017. "Daily happiness and stock returns: The case of Chinese company listed in the United States," Economic Modelling, Elsevier, vol. 64(C), pages 496-501.
    31. 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).
    32. Eberhardt, Markus & Bond, Stephen, 2009. "Cross-section dependence in nonstationary panel models: a novel estimator," MPRA Paper 17692, University Library of Munich, Germany.
    33. Siikanen, Milla & Baltakys, Kęstutis & Kanniainen, Juho & Vatrapu, Ravi & Mukkamala, Raghava & Hussain, Abid, 2018. "Facebook drives behavior of passive households in stock markets," Finance Research Letters, Elsevier, vol. 27(C), pages 208-213.
    34. Gur Huberman & Tomer Regev, 2001. "Contagious Speculation and a Cure for Cancer: A Nonevent that Made Stock Prices Soar," Journal of Finance, American Finance Association, vol. 56(1), pages 387-396, February.
    35. 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.
    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. Jingjian, Si & Xiangyun, Gao & Jinsheng, Zhou & Anjian, Wang & Xiaotian, Sun & Yiran, Zhao & Hongyu, Wei, 2023. "The impact of oil price shocks on energy stocks from the perspective of investor attention," Energy, Elsevier, vol. 278(PB).

    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. Reboredo, Juan C. & Ugolini, Andrea, 2018. "The impact of Twitter sentiment on renewable energy stocks," Energy Economics, Elsevier, vol. 76(C), pages 153-169.
    2. Aman, Hiroyuki & Moriyasu, Hiroshi, 2017. "Volatility and public information flows: Evidence from disclosure and media coverage in the Japanese stock market," International Review of Economics & Finance, Elsevier, vol. 51(C), pages 660-676.
    3. Agarwal, Shweta & Kumar, Shailendra & Goel, Utkarsh, 2019. "Stock market response to information diffusion through internet sources: A literature review," International Journal of Information Management, Elsevier, vol. 45(C), pages 118-131.
    4. Aman, Hiroyuki, 2013. "An analysis of the impact of media coverage on stock price crashes and jumps: Evidence from Japan," Pacific-Basin Finance Journal, Elsevier, vol. 24(C), pages 22-38.
    5. Szymon Lis, 2022. "Investor Sentiment in Asset Pricing Models: A Review," Working Papers 2022-14, Faculty of Economic Sciences, University of Warsaw.
    6. Dang, Tung Lam & Dang, Man & Hoang, Luong & Nguyen, Lily & Phan, Hoang Long, 2020. "Media coverage and stock price synchronicity," International Review of Financial Analysis, Elsevier, vol. 67(C).
    7. Daniele Ballinari & Simon Behrendt, 2021. "How to gauge investor behavior? A comparison of online investor sentiment measures," Digital Finance, Springer, vol. 3(2), pages 169-204, June.
    8. Hillert, Alexander & Jacobs, Heiko & Müller, Sebastian, 2018. "Journalist disagreement," Journal of Financial Markets, Elsevier, vol. 41(C), pages 57-76.
    9. Yousaf, Imran & Youssef, Manel & Goodell, John W., 2022. "Quantile connectedness between sentiment and financial markets: Evidence from the S&P 500 twitter sentiment index," International Review of Financial Analysis, Elsevier, vol. 83(C).
    10. Qadan, Mahmoud & Aharon, David Y. & Cohen, Gil, 2020. "Everybody likes shopping, including the US capital market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 551(C).
    11. Wu, Chunying & Xiong, Xiong & Gao, Ya & Zhang, Jin, 2022. "Does social media coverage deter firms from withholding bad news? Evidence from stock price crash risk," International Review of Financial Analysis, Elsevier, vol. 84(C).
    12. Benjamin Clapham & Michael Siering & Peter Gomber, 2021. "Popular News Are Relevant News! How Investor Attention Affects Algorithmic Decision-Making and Decision Support in Financial Markets," Information Systems Frontiers, Springer, vol. 23(2), pages 477-494, April.
    13. Autore, Don M. & Jiang, Danling, 2019. "The preholiday corporate announcement effect," Journal of Financial Markets, Elsevier, vol. 45(C), pages 61-82.
    14. Abudy, Menachem (Meni) & Mugerman, Yevgeny & Shust, Efrat, 2022. "The Winner Takes It All: Investor Sentiment and the Eurovision Song Contest," Journal of Banking & Finance, Elsevier, vol. 137(C).
    15. Xiong, Xiong & Meng, Yongqiang & Li, Xiao & Shen, Dehua, 2020. "Can overnight return really serve as a proxy for firm-specific investor sentiment? Cross-country evidence," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 64(C).
    16. Zhang, Tonghui & Yuan, Ying & Wu, Xi, 2020. "Is microblogging data reflected in stock market volatility? Evidence from Sina Weibo," Finance Research Letters, Elsevier, vol. 32(C).
    17. Renault, Thomas, 2017. "Intraday online investor sentiment and return patterns in the U.S. stock market," Journal of Banking & Finance, Elsevier, vol. 84(C), pages 25-40.
    18. Bonato, Matteo & Gkillas, Konstantinos & Gupta, Rangan & Pierdzioch, Christian, 2021. "A note on investor happiness and the predictability of realized volatility of gold," Finance Research Letters, Elsevier, vol. 39(C).
    19. Matteo Bonato & Konstantinos Gkillas & Rangan Gupta & Christian Pierdzioch, 2020. "Investor Happiness and Predictability of the Realized Volatility of Oil Price," Sustainability, MDPI, vol. 12(10), pages 1-11, May.
    20. Wang, Gang-Jin & Xiong, Lu & Zhu, You & Xie, Chi & Foglia, Matteo, 2022. "Multilayer network analysis of investor sentiment and stock returns," Research in International Business and Finance, Elsevier, vol. 62(C).

    More about this item

    Keywords

    Social media; Twitter; Energy Sector; Stock Prices;
    All these keywords.

    JEL classification:

    • G0 - Financial Economics - - General
    • Q4 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    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:pra:mprapa:114155. 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: Joachim Winter (email available below). General contact details of provider: https://edirc.repec.org/data/vfmunde.html .

    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.