Open AccessML-GAT:A Multilevel Graph Attention Model for Stock Prediction
Author(s) -
Kun Huang,
Xiaoming Li,
Fangyuan Liu,
Xiaoping Yang,
Wei Yu
Publication year - 2022
Publication title -
ieee access
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.587
H-Index - 127
ISSN - 2169-3536
DOI - 10.1109/access.2022.3199008
Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation
Stock market volatility research has long been the focus of industry and academia, and stock trend forecasting is challenging. Existing research focuses more on how to aggregate historical price features into graph networks, ignoring the effects of other information such as news and current events on forecasts. Most existing graph-based learning methods create stock graphs by manually constructing stock relationships, ignoring the complexity of stock relationships. Based on these, we propose a novel multilevel graph attention network (ML-GAT) for predicting the stock market trends. To be more specific, we initialize the node representations captured by each feature extraction module, then update the stock nodes in the isomorphic graph converted from Wikidata using ML-GAT, selectively aggregate the information of various relation types, aggregate the information to the node representation, and finally feed the result to a particular forecast layer for forecasting, completing the trend forecast of 423 stocks in the S&P 500 index and 286 stocks in the CSI 300 index. By comparing 5 popular approaches, the experimental research verifies ML-GAT’S state-of-the-art performance in prediction tasks. In comparison with the best performance benchmark model, the F1-score and accuracy increased by 11.82% and 12.6%, respectively, and the average daily return and Sharpe rate rose by 5.06 percent and 94.81 percent, respectively. More significantly, our model’s stock linkages are interpretable and consistent with real-world interactions.