Open Access

ARTICLE

An LSTM Based Forecasting for Major Stock Sectors Using COVID Sentiment

by Ayesha Jabeen¹, Sitara Afzal¹, Muazzam Maqsood¹, Irfan Mehmood², Sadaf Yasmin¹, Muhammad Tabish Niaz³, Yunyoung Nam^4,*

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 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

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.

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