This repository contains source codes of HATS, A Hierarchical Graph Attention Network for Stock Movement Prediction. As we conducted experiments on two different tasks, node classification and graph classification, we provide two different version of codes for each tasks. Please refer to our paper HATS: A Hierarchical Graph Attention Network for Stock Movement Prediction for further details.

Numpy 1.15.1
Tensorflow 1.11.0

Price-realted data and corporate relation data is used for HATS. We gathered both data for S&P 500 listed companies from 2013/02/08 to 2019/06/17 (1174 trading days in total). Price data are gathered from Yahoo Finance and corporate relation data are collected based on the information on Wikidata. Both datasets can be downloaded with the command below.

bash download.sh

You need to pass some arguments.
test_phase : phase that you want to test
save_dir : name of saving directories
data_type (only in graph_classification) : choose among ['S5CONS', 'S5ENRS', 'S5UTIL', 'S5FINL', 'S5INFT']

e.g.
make test_phase=1 save_dir=save data_type='S5CONS'