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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: email

(This article belongs to the Special Issue: Artificial Intelligence and Big Data in Entrepreneurship)

Computers, Materials & Continua 2021, 67(1), 1191-1206. https://doi.org/10.32604/cmc.2021.014598

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.

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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. https://doi.org/10.32604/cmc.2021.014598

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cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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