Stock market trend forecasting based on multiple textual features: A deep learning method

• Main Authors: HU, Zhenda, WANG, Zhaoxia, HO, Seng-Beng, TAN, Ah-Hwee
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2021
• Subjects: stock market trend forecasting; textual features; deep learning; sentiment analysis; Artificial Intelligence and Robotics; Databases and Information Systems
• Online Access: https://ink.library.smu.edu.sg/sis_research/6882; https://ink.library.smu.edu.sg/context/sis_research/article/7885/viewcontent/ICTAI_Stock_Market_Trend_Forecasting_Based_on_MultipleTextual_Features_A_Deep_Learning_Method_Final_Version.pdf
• Institution: Singapore Management University

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.