IDEAS home Printed from https://ideas.repec.org/a/mbr/jmonec/v15y2020i3p235-251.html
   My bibliography  Save this article

Forecasting Stock Price Movements Based on Opinion Mining and Sentiment Analysis: An Application of Support Vector Machine and Twitter Data

Author

Listed:
  • Sohrabi, Babak

    (Faculty of Management, University of Tehran)

  • Khalili Jafarabad, Ahmad

    (Faculty of Management, University of Tehran)

  • Hadizadeh, Ardalan

    (Faculty of Management, University of Tehran)

Abstract

Today, social networks are fast and dynamic communication intermediaries that are a vital business tool. This study aims at examining the views of those involved with Facebook stocks so that we can summarize their views to predict the general behavior of this stock and collectively consider possible Facebook stock price movements, and create a more accurate pattern compared to previous patterns. In this study, we have analyzed two statistical samples, the first being a large dataset containing a variety of tweets with an emotional tag. That is, it needed a set that had already been extracted from each individual tweet by a trusted human or machine. Consequently, we have collected posts on Facebook in an eighty-day period. In this study, we used a tagged dataset using Python's programming language and vector-to-word algorithm. The research results show that, we need stock change information, machine learning and sentiment analysis, and on paper we conclude that positive news about a company excites people to have positive opinions about it which in turn results in people encouraging each other to buy and hold stocks. Meanwhile, the opposite trend is also true, but everything will not always be easy and clear, and it is in areas of high complexity and mental uncertainty that the art of using the three elements mentioned above is evident.

Suggested Citation

  • Sohrabi, Babak & Khalili Jafarabad, Ahmad & Hadizadeh, Ardalan, 2020. "Forecasting Stock Price Movements Based on Opinion Mining and Sentiment Analysis: An Application of Support Vector Machine and Twitter Data," Journal of Money and Economy, Monetary and Banking Research Institute, Central Bank of the Islamic Republic of Iran, vol. 15(3), pages 235-251, July.
    • Handle: RePEc:mbr:jmonec:v:15:y:2020:i:3:p:235-251
    as

    Download full text from publisher

    File URL: http://jme.mbri.ac.ir/article-1-489-en.pdf
    Download Restriction: no
    File URL: http://jme.mbri.ac.ir/article-1-489-en.html
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. You, Wanhai & Guo, Yawei & Zhu, Huiming & Tang, Yong, 2017. "Oil price shocks, economic policy uncertainty and industry stock returns in China: Asymmetric effects with quantile regression," Energy Economics, Elsevier, vol. 68(C), pages 1-18.
    2. Timmermann, Allan & Granger, Clive W. J., 2004. "Efficient market hypothesis and forecasting," International Journal of Forecasting, Elsevier, vol. 20(1), pages 15-27.
    3. 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.
    Full references (including those not matched with items on IDEAS)

    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. Szymon Lis, 2022. "Investor Sentiment in Asset Pricing Models: A Review," Working Papers 2022-14, Faculty of Economic Sciences, University of Warsaw.
    2. Xu Gong & Mengjie Li & Keqin Guan & Chuanwang Sun, 2023. "Climate change attention and carbon futures return prediction," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 43(9), pages 1261-1288, September.
    3. Guerard, John, 2023. "Harry Markowitz: An appreciation," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1496-1501.
    4. Müller, Karsten, 2020. "German forecasters' narratives: How informative are German business cycle forecast reports?," Working Papers 23, German Research Foundation's Priority Programme 1859 "Experience and Expectation. Historical Foundations of Economic Behaviour", Humboldt University Berlin.
    5. Yingce Yang & Junjie Guo & Ruihong He, 2023. "The Asymmetric Impact of the Oil Price and Disaggregate Shocks on Economic Policy Uncertainty: Evidence From China," SAGE Open, , vol. 13(2), pages 21582440231, June.
    6. Goedde-Menke, Michael & Langer, Thomas & Pfingsten, Andreas, 2014. "Impact of the financial crisis on bank run risk – Danger of the days after," Journal of Banking & Finance, Elsevier, vol. 40(C), pages 522-533.
    7. David E. Allen & Michael McAleer & Abhay K. Singh, 2019. "Daily market news sentiment and stock prices," Applied Economics, Taylor & Francis Journals, vol. 51(30), pages 3212-3235, June.
    8. Yan Luo & Linying Zhou, 2020. "Textual tone in corporate financial disclosures: a survey of the literature," International Journal of Disclosure and Governance, Palgrave Macmillan, vol. 17(2), pages 101-110, September.
    9. Jiao Ji & Oleksandr Talavera & Shuxing Yin, 2018. "The Hidden Information Content: Evidence from the Tone of Independent Director Reports," Working Papers 2018-28, Swansea University, School of Management.
    10. Lixiang Wang & Wendi Hou & Yupei Liu, 2023. "How do co‐shareholding networks affect negative media coverage? Evidence from China," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 63(4), pages 4221-4249, December.
    11. Kamaladdin Fataliyev & Aneesh Chivukula & Mukesh Prasad & Wei Liu, 2021. "Stock Market Analysis with Text Data: A Review," Papers 2106.12985, arXiv.org, revised Jul 2021.
    12. Bennani, Hamza, 2018. "Media coverage and ECB policy-making: Evidence from an augmented Taylor rule," Journal of Macroeconomics, Elsevier, vol. 57(C), pages 26-38.
    13. Christopher N. Avery & Judith A. Chevalier & Richard J. Zeckhauser, 2016. "The "CAPS" Prediction System and Stock Market Returns," Review of Finance, European Finance Association, vol. 20(4), pages 1363-1381.
    14. William Ginn, 2022. "Climate Disasters and the Macroeconomy: Does State-Dependence Matter? Evidence for the US," Economics of Disasters and Climate Change, Springer, vol. 6(1), pages 141-161, March.
    15. Rui Liu & Jiayou Liang & Haolong Chen & Yujia Hu, 2024. "Analyst Reports and Stock Performance: Evidence from the Chinese Market," Papers 2411.08726, arXiv.org.
    16. Keval Amin & Erica Harris, 2022. "The Effect of Investor Sentiment on Nonprofit Donations," Journal of Business Ethics, Springer, vol. 175(2), pages 427-450, January.
    17. Femg, Xunan & Johansson, Anders C., 2019. "News or Noise? The Information Content of Social Media in China," Stockholm School of Economics Asia Working Paper Series 2019-52, Stockholm School of Economics, Stockholm China Economic Research Institute.
    18. King, Timothy & Srivastav, Abhishek & Williams, Jonathan, 2016. "What's in an education? Implications of CEO education for bank performance," Journal of Corporate Finance, Elsevier, vol. 37(C), pages 287-308.
    19. Kirtac, Kemal & Germano, Guido, 2024. "Sentiment trading with large language models," Finance Research Letters, Elsevier, vol. 62(PB).
    20. André Betzer & Jan Philipp Harries, 2022. "How online discussion board activity affects stock trading: the case of GameStop," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 36(4), pages 443-472, December.

    More about this item

    Keywords

    Social Networking; Stock Prediction; Group Emotion; Collective Emotion; Sentiment Analysis; Opinion Mining; Neural Network;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • M15 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Administration - - - IT Management

    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:mbr:jmonec:v:15:y:2020:i:3:p:235-251. 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: M. E. (email available below). General contact details of provider: https://edirc.repec.org/data/mbcbiir.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.