This repository releases a comprehensive dataset for stock movement prediction from historical stock prices. Please cite the following paper bib if you use this dataset, Alireza Jafari and Saman Haratizadeh. 2022. GCNET: graph-based prediction of stock price movement using graph convolutional network
Code of paper is here (GCNET-Code).
Our dataset contains the historical data for 100 stocks with the largest market capitalization in the Nasdaq index from 01/01/2011 to 01/01/2021. The data in 15/09/2020 to 29/09/2020 interval is used for validation and to set the hyper-parameters. The test period is from 09/30/2020 to 12/30/2020 which includes 5952 records.
Format: CSV
Entries: date, open price, high price, low price, close price, adjust close price, volume
@article{JAFARI2022105452,
title = {GCNET: Graph-based prediction of stock price movement using graph convolutional network},
journal = {Engineering Applications of Artificial Intelligence},
volume = {116},
pages = {105452},
year = {2022},
issn = {0952-1976},
doi = {https://doi.org/10.1016/j.engappai.2022.105452},
url = {https://www.sciencedirect.com/science/article/pii/S0952197622004420},
author = {Alireza Jafari and Saman Haratizadeh}
}