/ADGAT

Modeling the Momentum Spillover Effect for Stock Prediction via Attribute-Driven Graph Attention Networks

Primary LanguagePythonMIT LicenseMIT

Modeling the Momentum Spillover Effect for Stock Prediction via Attribute-Driven Graph Attention Networks

Code & Data for the stock prediction model in our paper: Modeling the Momentum Spillover Effect for Stock Prediction via Attribute-Driven Graph Attention Networks.

Environment

Python 3.7.6 & Pytorch 1.5.1

Run

$ python main.py --device=0

Make sure that the GPU is used to reproduce our experiments.

Data

All the preprocessed data can be found at ./data.

Selected Stock

The selected 198 tickers can be found at ./raw_data/stocks.txt

Transcational Data

The raw transcational data is about 1.31Gb. Link and preprocess code will be released soon.

Sentiment Indicators

The news data are not provided in our repository due to copyright issues.

We use financial news from Reuters and Bloomberg over the period from 2011 to 2013, released by Ding et al. ("Using structured events to predict stock price movement: An empirical investigation." EMNLP. 2014.)

The Loughran-McDonald Master Dictionary (https://sraf.nd.edu/textual-analysis/resources/) is used to extract sentiment from financial articles.

Company Relations

Firm relations can be found at ./raw_data/relations/*. The Company Relations are collected from S&P Capical IQ (https://www.capitaliq.com/). The first row is the target stock tickers, and following rows are firms that has specific relation with that frim.

Contact

chengrui0108@hotmail.com