Affiliation:
1. Department of Computer Science, Wenzhou Kean University, Wenzhou, Zhejiang, China
Abstract
These days, the vast amount of data generated on the Internet is a new treasure trove for investors. They can utilize text mining and sentiment analysis techniques to reflect investors’ confidence in specific stocks in order to make the most accurate decision. Most previous research just sums up the text sentiment score on each natural day and uses such aggregated score to predict various stock trends. However, the natural day aggregated score may not be useful in predicting different stock trends. Therefore, in this research, we designed two different time divisions: 0:00t∼0:00t+1 and 9:30t∼9:30t+1 to study how tweets and news from the different periods can predict the next-day stock trend. 260,000 tweets and 6,000 news from Service stocks (Amazon, Netflix) and Technology stocks (Apple, Microsoft) were selected to conduct the research. The experimental result shows that opening hours division (9:30t∼9:30t+1) outperformed natural hours division (0:00t∼0:00t+1).
Reference51 articles.
1. State-of-the-art in stock prediction techniques;Agrawal;International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering,2013
2. SutteARIMA: short-term forecasting method, a case: Covid-19 and stock market in Spain;Ahmar;Science of the Total Environment,2020
3. Recent advances in stock market prediction using text mining: a survey;Alzazah;E-Business-Higher Education and Intelligence Applications,2020
4. Finbert: financial sentiment analysis with pre-trained language models;Araci,2019
5. X-CAPM: an extrapolative capital asset pricing model;Barberis;Journal of Financial Economics,2015
Cited by
10 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献