  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.
