Stock movement prediction via gated recurrent unit network based on reinforcement learning with incorporated attention mechanisms

Abstract

The recent advances usually mine market information from the chaotic data to conduct a stock movement prediction task. However, the current stock price movement prediction approaches mainly compute attention weighted sum of the global contextual semantic embeddings, which fails to combine local word-level or char-level ones to jointly learn news-level representation. Moreover, for Chinese stock price movement prediction task, some collected news texts are chaotic even irrelevant to the target stock. It suggests that the models need filter some news-level representations (viewed as noises) to enhance the performance. To that aim, we develop a novel stock price movement prediction network via bidirectional gated recurrent unit (GRU) network based on reinforcement learning (RL) with incorporated attention mechanism. In specific, to reduce the noise of news texts and learn news-level representation with more abundant semantics, two novel attention mechanisms respectively based on add and dot operation were first proposed in this work. We then design a novel GRU structure based on RL to filter some irrelated news-level representations (i.e., news-level noises) and capture abundant long-term dependencies. Finally, the experimental results show that the proposed model far outperforms the recent advances and achieves state-of-the-art performances.