Stock movement predictive network via incorporative attention mechanisms based on tweet and historical prices

摘要

The recent advances usually attempt to mine the effective market information from the chaotic data and learn multilevel representations by using attention mechanisms to conduct a stock prediction task. However, such methods usually lack the full utilization of local semantic embedding which contains the abundant textual semantics information. Moreover, these models suffer from the severe noise diffusion in contextual embeddings from a sequence after passing through the RNN. The noises diffusion constrains the performance of the proposed methods. In this work, we propose a stock movement predictive network via incorporative attention mechanisms. The core innovation is that the incorporative attention combines local and contextual attention mechanisms to clean the contextual embeddings by using local semantics. As a result, the attention effectively reduce the noises in the constructed higher-level representations and enhance the performance. Moreover, the local semantics and context are merged into the constructed higher-level representations which provide more abundant local semantic and contextual information. The experimental results demonstrate the state-of-the-art performance of the proposed approach on tweet and historical price dataset. (C) 2020 Published by Elsevier B.V.