An efficient hybrid stock trend prediction system during COVID-19 pandemic based on stacked-LSTM and news sentiment analysis

• Published in: Multimedia tools and applications 2023-07, Vol.82 (16), p.23945-23977
• Main Authors: Sharaf, Marwa, Hemdan, Ezz El-Din, El-Sayed, Ayman, El-Bahnasawy, Nirmeen A.
• Format: Article
• Language: English
• Subjects: Computer Communication Networks; Computer Science; Coronaviruses; COVID-19; Data mining; Data Structures and Information Theory; Global economy; Hybrid systems; Impact analysis; Machine learning; Multimedia Information Systems; News; Securities markets; Sentiment analysis; Special Purpose and Application-Based Systems; Stock exchanges; Viral diseases
• ISSN: 1380-7501; 1573-7721
• DOI: 10.1007/s11042-022-14216-w

The coronavirus is an irresistible virus that generally influences the respiratory framework. It has an effective impact on the global economy specifically, on the financial movement of stock markets. Recently, an accurate stock market prediction has been of great interest to investors. A sudden change in the stock movement due to COVID -19 appearance causes some problems for investors. From this point, we propose an efficient system that applies sentiment analysis of COVID-19 news and articles to extract the final impact of COVID-19 on the financial stock market. In this paper, we propose a stock market prediction system that extracts the stock movement with the COVID spread. It is important to predict the effect of these diseases on the economy to be ready for any disease change and protect our economy. In this paper, we apply sentimental analysis to stock news headlines to predict the daily future trend of stock in the COVID-19 period. Also, we use machine learning classifiers to predict the final impact of COVID-19 on some stocks such as TSLA, AMZ, and GOOG stock. For improving the performance and quality of future trend predictions, feature selection and spam tweet reduction are performed on the data sets. Finally, our proposed system is a hybrid system that applies text mining on social media data mining on the historical stock dataset to improve the whole prediction performance. The proposed system predicts stock movement for TSLA, AMZ, and GOOG with average prediction accuracy of 90%, 91.6%, and 92.3% respectively.