📈 (Dataset Available) -- ChatGPT Informed Graph Neural Network for Stock Movement Prediction 📈

• This repository houses the datasets/resources used in our paper ChatGPT Informed Graph Neural Network for Stock Movement Prediction.
• This research introduces a novel framework that leverages ChatGPT's graph inference capabilities to enhance Graph Neural Networks (GNN).
• Our model shows superior performance in both (1) stock movement prediction and (2) portfolio construction.
• Dive in to find the datasets, code samples, and more!
• 📄 Paper: [[Link to the paper]](https://arxiv.org/abs/2306.03763)

🚀 Get Started

The data can be found in the Data folder, which contains two files:

• tickertraindata.json: This file holds the data utilized for training and validation of our model.
• tickertestdata.json: This file contains the data used for model evaluation.

import pandas as pd
import json
traindata = pd.readjson('./Data/tickertraindata.json') testdata = pd.readjson('./Data/tickertestdata.json')

The Affected Companies column provides two key insights:

• Companies that ChatGPT predicts will be influenced by the financial news.
• The sentiment indicating the nature of the impact on these companies (e.g., positive or negative).

🔗 Citation:

We encourage collaboration and use of this dataset for further advancements in stock prediction using deep learning. If you find this resource useful, kindly cite our paper. Happy researching!

@article{chen2023chatgpt,
  title={ChatGPT Informed Graph Neural Network for Stock Movement Prediction},
  author={Chen, Zihan and Zheng, Lei Nico and Lu, Cheng and Yuan, Jialu and Zhu, Di},
  journal={arXiv preprint arXiv:2306.03763},
  year={2023}
}