A structured multi-head attention prediction method based on heterogeneous financial data

• Published in: PeerJ. Computer science Vol. 9; p. e1653
• Main Authors: Zhao, Cheng, Li, Fangyong, Peng, Zhe, Zhou, Xiao, Zhuge, Yan
• Format: Journal Article
• Language: English
• Published: United States PeerJ. Ltd 17.11.2023; PeerJ, Inc; PeerJ Inc
• Subjects: Algorithms; Customization; Data mining; Data processing; Deep learning; Feature extraction; Financial markets; Heterogeneous financial dat; Information sources; Institutional investments; Investor behavior; Literature reviews; Machine learning; Neural networks; Public opinion; Securities markets; Stock exchanges; Stock prediction; Stock prices; Structured multi-head attention; Trends; China; Heterogeneous financial dat; Stock prediction; Structured multi-head attention
• ISSN: 2376-5992; 2376-5992
• DOI: 10.7717/peerj-cs.1653

The diverse characteristics of heterogeneous data pose challenges in analyzing combined price and volume data. Therefore, appropriately handling heterogeneous financial data is crucial for accurate stock prediction. This article proposes a model that applies customized data processing methods tailored to the characteristics of different types of heterogeneous financial data, enabling finer granularity and improved feature extraction. By utilizing the structured multi-head attention mechanism, the model captures the impact of heterogeneous financial data on stock price trends by extracting data information from technical, financial, and sentiment indicators separately. Experimental results conducted on four representative individual stocks in China's A-share market demonstrate the effectiveness of the proposed method. The model achieves an average MAPE of 1.378%, which is 0.429% lower than the benchmark algorithm. Moreover, the backtesting return rate exhibits an average increase of 28.56%. These results validate that the customized preprocessing method and structured multi-head attention mechanism can enhance prediction accuracy by attending to different types of heterogeneous data individually.