Multi-Element Hierarchical Attention Capsule Network for Stock Prediction

Abstract

Stock prediction is a challenging task concerned by researchers due to its considerable returns. It is difficult because of the high randomness in the stock market. Stock price movement is mainly related to the capital situation and hot events. In recent years, researchers improved prediction accuracy with news and social media. However, the existing methods do not take into account the different influences of events. To solve this problem, we propose a multi-element hierarchical attention capsule network, which consists of two components. The former component, multi-element hierarchical attention, quantifies the importance of valuable information contained in multiple news and social media through its weights assignment process. And the latter component, capsule network, learns more context information from the events through its vector representation in the hidden layer. Moreover, we construct a combined data set to maintain the complementarity between social media and news. Finally, we achieve better results than baselines, and experiments show that our model improves prediction accuracy by quantifying the different influences of events.

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