An interval constraint-based trading strategy with social sentiment for the stock market

Mingchen Li (), Kun Yang (), Wencan Lin (), Yunjie Wei () and Shouyang Wang ()
Additional contact information
Mingchen Li: Chinese Academy of Sciences
Kun Yang: Chinese Academy of Sciences
Wencan Lin: Chinese Academy of Sciences
Yunjie Wei: Chinese Academy of Sciences
Shouyang Wang: Chinese Academy of Sciences

Financial Innovation, 2024, vol. 10, issue 1, 1-31

Abstract: 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.

Keywords: Stock price forecasting; Deep learning; Sentiment analysis; Trading strategy; COVID-19 era (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1186/s40854-023-00567-2 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:fininn:v:10:y:2024:i:1:d:10.1186_s40854-023-00567-2

Ordering information: This journal article can be ordered from
http://www.springer. ... nomics/journal/40589

DOI: 10.1186/s40854-023-00567-2

Access Statistics for this article

Financial Innovation is currently edited by J. Leon Zhao and Zongyi

More articles in Financial Innovation from Springer, Southwestern University of Finance and Economics
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().