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

ABSTRACT: 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 effective

SUBMITTER: Zhao C 

PROVIDER: S-EPMC10703059 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature