A hierarchical attention network for stock prediction based on attentive multi-view news learning

Stock prediction with news released on media platform is helpful for investors to make good investment decisions. Recent researches are generally based on single news view, e.g., headline or body, as a predic-tive indicator and thus information received is insufficient or incomplete which also lacks of study on market information, then bring low performances of models. In this research, we propose a hierarchical attention network based on attentive multi-view news learning (NMNL) to excavate more useful infor-mation from news and the stock market for stock prediction. The core of our approach is a news encoder and a market information encoder. In the news encoder, we learn multi-view news information represen-tation from news headlines, bodies and sentiments by regarding them as three independent parts. We find that the combination of headline, body and sentiment outperforms conventional models on single news view. In the market information encoder, we employ the attention mechanism to capture pivotal news information and combine technical indicators to represent representative market information. In addition, a temporal auxiliary based on Bi-directional Long Short-Term Memory (Bi-LSTM) model is used to generate the contextual market information for stock prediction. Extensive experiments demonstrate the superiority of NMNL, which outperforms state-of-the-art stock prediction solutions.(c) 2022 Elsevier B.V. All rights reserved.

Automated summary

This research proposes a hierarchical attention network based on attentive multi-view news learning (NMNL) to extract more useful information for stock prediction from news and the stock market. Extensive experiments demonstrate the superiority of NMNL over state-of-the-art stock prediction solutions.