Multi-feature fusion stock prediction based on knowledge graph

• Published in: Electronic library Vol. 42; no. 3; pp. 455 - 482
• Main Authors: Liu, Zhenghao, Qian, Yuxing, Lv, Wenlong, Fang, Yanbin, Liu, Shenglan
• Format: Journal Article
• Language: English
• Published: Oxford Emerald Publishing Limited 27.06.2024; Emerald Group Publishing Limited
• Subjects: Algorithms; Artificial intelligence; Bank technology; Behavior; Behavioral economics; Deep learning; Efficient markets; Forecasting; Graphical representations; Internet stocks; Investments; Investors; Knowledge; Knowledge representation; Neural networks; News; Researchers; Resistance (Psychology); Securities markets; Semantics; Semiotics; Sentiment analysis; Short Term Memory; Social Media; Social networks; Stock exchanges; Stock prices; Teaching Methods; Time series; Volatility; Knowledge graph embedding; Sentiment analysis; Muti-feature fusion; Stock prediction; Knowledge graph; Relevant stocks
• ISSN: 0264-0473; 0264-0473; 1758-616X
• DOI: 10.1108/EL-02-2023-0053

Purpose Stock prices are subject to the influence of news and social media, and a discernible co-movement pattern exists among multiple stocks. Using a knowledge graph to represent news semantics and establish connections between stocks is deemed essential and viable. Design/methodology/approach This study presents a knowledge-driven framework for predicting stock prices. The framework integrates relevant stocks with the semantic and emotional characteristics of textual data. The authors construct a stock knowledge graph (SKG) to extract pertinent stock information and use a knowledge graph representation model to capture both the relevant stock features and the semantic features of news articles. Additionally, the authors consider the emotional characteristics of news and investor comments, drawing insights from behavioral finance theory. The authors examined the effectiveness of these features using the combined deep learning model CNN+LSTM+Attention. Findings Experimental results demonstrate that the knowledge-driven combined feature model exhibits significantly improved predictive accuracy compared to single-feature models. Originality/value The study highlights the value of the SKG in uncovering potential correlations among stocks. Moreover, the knowledge-driven multi-feature fusion stock forecasting model enhances the prediction of stock trends for well-known enterprises, providing valuable guidance for investor decision-making.