Publication Type
Conference Proceeding Article
Version
acceptedVersion
Publication Date
11-2021
Abstract
Stock market trend forecasting is a valuable and challenging research task for both industry and academia. In order to explore the influence of stock news information on the stock market trend, a textual embedding construction method is proposed to encode multiple textual features, including topic features, sentiment features, and semantic features extracted from stock news textual content. In addition, a deep learning method is designed by using financial data and multiple textual features obtained from multiple news textual embeddings for short-term stock market trend prediction. For evaluation, extensive experiments on real stock market data are conducted. The experimental results illustrate that the proposed method can enhance the performance of predicting stock market trend by obtaining effective information from stock news.
Keywords
stock market trend forecasting, textual features, deep learning, sentiment analysis
Discipline
Artificial Intelligence and Robotics | Databases and Information Systems
Research Areas
Data Science and Engineering; Intelligent Systems and Optimization
Publication
The 33rd IEEE International Conference on Tools with Artificial Intelligence
Publisher
IEEE
City or Country
US
Citation
HU, Zhenda; WANG, Zhaoxia; HO, Seng-Beng; and TAN, Ah-Hwee.
Stock market trend forecasting based on multiple textual features: A deep learning method. (2021). The 33rd IEEE International Conference on Tools with Artificial Intelligence.
Available at: https://ink.library.smu.edu.sg/sis_research/6882
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.