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- Title
An interval constraint-based trading strategy with social sentiment for the stock market.
- Authors
Li, Mingchen; Yang, Kun; Lin, Wencan; Wei, Yunjie; Wang, Shouyang
- Abstract
Developing effective strategies to earn excess returns in the stock market is a cutting-edge topic in the field of economics. At the same time, stock price forecasting that supports trading strategies is considered one of the most challenging tasks. Therefore, this study analyzes and extracts news media data, expert comments, social opinion data, and pandemic text data using natural language processing, and then combines the data with a deep learning model to forecast future stock price patterns based on historical stock prices. An interval constraint-based trading strategy is constructed. Using data from several typical stocks in the Chinese stock market during the COVID-19 period, the empirical studies and trading simulations show, first, that the sentiment composite index and the deep learning model can improve the accuracy of stock price forecasting. Second, the interval constraint-based trading strategy based on the proposed approach can effectively enhance returns and thus, can assist investors in decision-making.
- Subjects
STOCK price forecasting; NATURAL language processing; MARKET sentiment; COVID-19 pandemic; ABNORMAL returns; BULL markets; ECONOMICS education
- Publication
Financial Innovation, 2024, Vol 10, p1
- ISSN
2199-4730
- Publication type
Article
- DOI
10.1186/s40854-023-00567-2