MENDNet: Memory-Enhanced Dependency Network for Multistock Movement Prediction.

Abstract

The stock movement prediction task has long been treated as one of the most crucial tasks for financial data mining. Unfortunately, prior arts may fail to capture the intricate nature of severe stock fluctuations, not to mention the difficulties in extracting reliable cues from massive, noisy financial news. In this case, it becomes more challenging to explore the distinctive evolving dynamics of individual stocks and discover deep-seated interstock dependencies within the market. To tackle these challenges, in this article, we propose a novel Memory-ENhanced Dependency Network, dubbed as MENDNet, for the multistock movement prediction task. Specifically, we first leverage the recent price movement from a dual-view perspective, providing a comprehensive depiction of each stock. Afterward, to mitigate noise information and the temporal effect of news cues, we assess their contribution based on the stock price volatility with a time-aware updating function. Along this line, we develop a well-elaborated memory structure to store selective news history for each stock, so that history embeddings can be dynamically estimated by attentively aggregating exclusive history information. Finally, we further delve into the evolving interdependencies among different stocks without relying on additional prior knowledge via a dependency-aware graph attention network. Extensive experiments on three real-world datasets validate the effectiveness of our proposed MENDNet framework compared with state-of-the-art baseline methods.