Vol.67, No.1, 2021, pp.1191-1206, doi:10.32604/cmc.2021.014598
OPEN ACCESS
ARTICLE
An LSTM Based Forecasting for Major Stock Sectors Using COVID Sentiment
  • Ayesha Jabeen1, Sitara Afzal1, Muazzam Maqsood1, Irfan Mehmood2, Sadaf Yasmin1, Muhammad Tabish Niaz3, Yunyoung Nam4,*
1 Department of Computer Science, COMSATS University Islamabad, Attock Campus, Pakistan
2 Department of Media Design and Technology, Faculty of Engineering & Informatics, University of Bradford, Bradford, BD7 1AZ, UK
3 Department of Smart Device Engineering, School of Intelligent Mechatronics Engineering, Sejong University, Seoul, Korea
4 Department of Computer Science & Engineering, Soonchunhyang University, Asan, 36538, Korea
* Corresponding Author: Yunyoung Nam. Email:
(This article belongs to this Special Issue: Artificial Intelligence and Big Data in Entrepreneurship)
Received 02 October 2020; Accepted 29 November 2020; Issue published 12 January 2021
Abstract
Stock market forecasting is an important research area, especially for better business decision making. Efficient stock predictions continue to be significant for business intelligence. Traditional short-term stock market forecasting is usually based on historical market data analysis such as stock prices, moving averages, or daily returns. However, major events’ news also contains significant information regarding market drivers. An effective stock market forecasting system helps investors and analysts to use supportive information regarding the future direction of the stock market. This research proposes an efficient model for stock market prediction. The current proposed study explores the positive and negative effects of coronavirus events on major stock sectors like the airline, pharmaceutical, e-commerce, technology, and hospitality. We use the Twitter dataset for calculating the coronavirus sentiment with a Long Short-Term Memory (LSTM) model to improve stock prediction. The LSTM has the advantage of analyzing relationship between time-series data through memory functions. The performance of the system is evaluated by Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE). The results show that performance improves by using coronavirus event sentiments along with the LSTM prediction model.
Keywords
Business intelligence; decision making; stock prediction; long short-term memory; COVID-19; event sentiment
Cite This Article
A. Jabeen, S. Afzal, M. Maqsood, I. Mehmood, S. Yasmin et al., "An lstm based forecasting for major stock sectors using covid sentiment," Computers, Materials & Continua, vol. 67, no.1, pp. 1191–1206, 2021.
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