IDEAS home Printed from https://ideas.repec.org/a/eee/ininma/v50y2020icp432-451.html
   My bibliography  Save this article

A local and global event sentiment based efficient stock exchange forecasting using deep learning

Author

Listed:
  • Maqsood, Haider
  • Mehmood, Irfan
  • Maqsood, Muazzam
  • Yasir, Muhammad
  • Afzal, Sitara
  • Aadil, Farhan
  • Selim, Mahmoud Mohamed
  • Muhammad, Khan

Abstract

Stock exchange forecasting is an important aspect of business investment plans. The customers prefer to invest in stocks rather than traditional investments due to high profitability. The high profit is often linked with high risk due to the nonlinear nature of data and complex economic rules. The stock markets are often volatile and change abruptly due to the economic conditions, political situation and major events for the country. Therefore, to investigate the effect of some major events more specifically global and local events for different top stock companies (country-wise) remains an open research area. In this study, we consider four countries- US, Hong Kong, Turkey, and Pakistan from developed, emerging and underdeveloped economies’ list. We have explored the effect of different major events occurred during 2012–2016 on stock markets. We use the Twitter dataset to calculate the sentiment analysis for each of these events. The dataset consists of 11.42 million tweets that were used to determine the event sentiment. We have used linear regression, support vector regression and deep learning for stock exchange forecasting. The performance of the system is evaluated using the Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). The results show that performance improves by using the sentiment for these events.

Suggested Citation

  • Maqsood, Haider & Mehmood, Irfan & Maqsood, Muazzam & Yasir, Muhammad & Afzal, Sitara & Aadil, Farhan & Selim, Mahmoud Mohamed & Muhammad, Khan, 2020. "A local and global event sentiment based efficient stock exchange forecasting using deep learning," International Journal of Information Management, Elsevier, vol. 50(C), pages 432-451.
    • Handle: RePEc:eee:ininma:v:50:y:2020:i:c:p:432-451
    DOI: 10.1016/j.ijinfomgt.2019.07.011
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0268401218313008
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ijinfomgt.2019.07.011?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

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


    Cited by:

    1. Ransome Epie Bawack & Samuel Fosso Wamba & Kevin Daniel André Carillo & Shahriar Akter, 2022. "Artificial intelligence in E-Commerce: a bibliometric study and literature review," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(1), pages 297-338, March.
    2. Thierry Warin & Aleksandar Stojkov, 2023. "“Decoding” Policy Perspectives: Structural Topic Modeling of European Central Bankers’ Speeches," JRFM, MDPI, vol. 16(7), pages 1-23, July.
    3. Jabeur, Sami Ben & Ballouk, Houssein & Mefteh-Wali, Salma & Omri, Anis, 2022. "Forecasting the macrolevel determinants of entrepreneurial opportunities using artificial intelligence models," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
    4. Delia DiaconaÅŸu & Seyed Mehdian & Ovidiu Stoica, 2023. "The Global Stock Market Reactions to the 2016 U.S. Presidential Election," SAGE Open, , vol. 13(2), pages 21582440231, June.
    5. Cano-Marin, Enrique & Mora-Cantallops, Marçal & Sánchez-Alonso, Salvador, 2023. "Twitter as a predictive system: A systematic literature review," Journal of Business Research, Elsevier, vol. 157(C).
    6. Bui Thanh Khoa & Tran Trong Huynh & Vo Dinh Nhat Truong & Le Vu Truong & Do Bui Xuan Cuong & Tran Khanh, 2023. "Minimal Spanning Tree application to determine market correlation structure," HO CHI MINH CITY OPEN UNIVERSITY JOURNAL OF SCIENCE - ENGINEERING AND TECHNOLOGY, HO CHI MINH CITY OPEN UNIVERSITY JOURNAL OF SCIENCE, HO CHI MINH CITY OPEN UNIVERSITY, vol. 13(1), pages 64-71.
    7. Sungwoo Kang & Jong-Kook Kim, 2023. "Using a Deep Learning Model to Simulate Human Stock Trader's Methods of Chart Analysis," Papers 2304.14870, arXiv.org, revised Apr 2024.
    8. Fatima Iqbal & Dr. Sadia Farooq & Dr. Sajid Nazir, 2023. "Herd behavior in stock markets during COVID’ 19 Pandemic: A machine learning approach," Journal of Policy Research (JPR), Research Foundation for Humanity (RFH), vol. 9(2), pages 268-273.

    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:eee:ininma:v:50:y:2020:i:c:p:432-451. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/international-journal-of-information-management .

    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.