[论文解读] HATS: A Hierarchical Graph Attention Network for Stock Movement Prediction

Raehyun Kim, Chan Ho So+4 more|arXiv (Cornell University)|Aug 7, 2019

Stock Market Forecasting Methods参考文献 24被引用 111

一句话总结

HATS 引入了一个分层图注意力网络，该网络有选择地聚合来自多种关系类型的信息，以预测股票走势和市场指数走势，优于基线。

ABSTRACT

Many researchers both in academia and industry have long been interested in the stock market. Numerous approaches were developed to accurately predict future trends in stock prices. Recently, there has been a growing interest in utilizing graph-structured data in computer science research communities. Methods that use relational data for stock market prediction have been recently proposed, but they are still in their infancy. First, the quality of collected information from different types of relations can vary considerably. No existing work has focused on the effect of using different types of relations on stock market prediction or finding an effective way to selectively aggregate information on different relation types. Furthermore, existing works have focused on only individual stock prediction which is similar to the node classification task. To address this, we propose a hierarchical attention network for stock prediction (HATS) which uses relational data for stock market prediction. Our HATS method selectively aggregates information on different relation types and adds the information to the representations of each company. Specifically, node representations are initialized with features extracted from a feature extraction module. HATS is used as a relational modeling module with initialized node representations. Then, node representations with the added information are fed into a task-specific layer. Our method is used for predicting not only individual stock prices but also market index movements, which is similar to the graph classification task. The experimental results show that performance can change depending on the relational data used. HATS which can automatically select information outperformed all the existing methods.

研究动机与目标

• 研究不同类型的企业关系如何影响股票走势预测。
• 开发一个从多种关系类型中有选择地聚合信息的模型。
• 实现利用关系数据进行单只股票走势预测和市场指数（图级）预测。

提出的方法

• 提出 HATS，一个分层图注意力网络，它计算关系类型特定的摘要并将其融入到节点表征中。
• 使用两层分层注意力机制：第一层在每种关系类型内对邻居进行关注，第二层对关系类型进行关注以形成聚合的节点表征。
• 用特征提取模块初始化节点特征（股票预测使用 LSTM，指数预测使用 GRU）。
• 通过对更新后的节点表征应用 softmax 分类器，整合一个面向单个股票预测的任务特定模块。
• 应用图池化（均值池化）来获得用于指数预测的图表示，并与图编码特征结合进行最终预测。）

实验结果

研究问题

• RQ1 哪些类型的关系数据最有助于股票走势预测？
• RQ2 一个在关系类型之间有选择地聚合信息的模型，是否能在节点和图（指数）预测任务上都超越基线？
• RQ3 分层注意力机制如何影响关系类型和邻居信息的利用？

主要发现

• 在使用有选择地选择的关系时，HATS 在股票走势预测方面超越现有基线。
• 当包含有用的关系时，该方法实现了更高的性能；无关的关系可能降低准确性。
• 对于指数走势预测，图池化结合关系特征在预测能力上优于基线。
• 研究表明性能随关系数据的质量和相关性而变化。
• 本文在实际指标上报告了显著提升：Sharpe 比率相对于基线提高 19.8%，F1-score 提高 3%。