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

Author

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  • 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
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    Citations

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    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. Mehmet Sahiner, 2024. "Volatility Spillovers and Contagion During Major Crises: An Early Warning Approach Based on a Deep Learning Model," Computational Economics, Springer;Society for Computational Economics, vol. 63(6), pages 2435-2499, June.
    3. 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.
    4. 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).
    5. 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.
    6. 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).
    7. 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.
    8. 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.
    9. 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.

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