Zhongtian Sun zhongtian.sun@durham.ac.uk
PGR Student Doctor of Philosophy
MONEY: Ensemble learning for stock price movement prediction via a convolutional network with adversarial hypergraph model
Sun, Zhongtian; Harit, Anoushka; Cristea, Alexandra I.; Wang, Jingyun; Lio, Pietro
Authors
Anoushka Harit anoushka.harit@durham.ac.uk
PGR Student Master of Science
Alexandra I. Cristea
Dr Jingyun Wang jingyun.wang@durham.ac.uk
Assistant Professor
Pietro Lio
Abstract
Stock price prediction is challenging in financial investment, with the AI boom leading to increased interest from researchers. Despite these recent advances, many studies are limited to capturing the time series characteristics of price movement via recurrent neural networks (RNNs) but neglect other critical relevant factors, such as industry, shareholders, and news. On the other hand, graph neural networks have been applied to a broad range of tasks due to their superior performance in capturing complex relations among entities and representation learning. This paper investigates the effectiveness of using graph neural networks for stock price movement prediction. Inspired by a recent study, we capture the complex group-level information (co-movement of similar companies) via hypergraphs. Unlike other hypergraph studies, we also use a graph model to learn pairwise relations. Moreover, we are the first to demonstrate that this simple graph model should be applied before using RNNs, rather than later, as prior research suggested. In this paper, the long-term dependencies of similar companies can be learnt by the next RNNs, which augments their predictability. We also apply adversarial training to capture the stochastic nature of the financial market and enhance the generalisation of the proposed model. Hence, we contribute with a novel ensemble learning framework to predict stock price movement, named MONEY. It is comprised of (a) a Graph Convolution Network (GCN), representing pairwise industry and price information and (b) a hypergraph convolution network for group-oriented information transmission via hyperedges with adversarial training by adding perturbations on inputs before the last prediction layer. Real-world data experiments demonstrate that MONEY significantly outperforms, on average, the state-of-the-art methods and performs particularly well in the bear market.
Citation
Sun, Z., Harit, A., Cristea, A. I., Wang, J., & Lio, P. (2023). MONEY: Ensemble learning for stock price movement prediction via a convolutional network with adversarial hypergraph model. AI open, 4, 165-174. https://doi.org/10.1016/j.aiopen.2023.10.002
Journal Article Type | Article |
---|---|
Acceptance Date | Oct 18, 2023 |
Online Publication Date | Oct 20, 2023 |
Publication Date | Oct 20, 2023 |
Deposit Date | Jan 10, 2024 |
Publicly Available Date | Jan 10, 2024 |
Journal | AI Open |
Peer Reviewed | Peer Reviewed |
Volume | 4 |
Pages | 165-174 |
DOI | https://doi.org/10.1016/j.aiopen.2023.10.002 |
Keywords | Software; Information Systems; Human-Computer Interaction; Computer Vision and Pattern Recognition; Computer Science Applications; Artificial Intelligence |
Public URL | https://durham-repository.worktribe.com/output/2118511 |
Files
Published Journal Article
(1.8 Mb)
PDF
Licence
http://creativecommons.org/licenses/by-nc-nd/4.0/
Publisher Licence URL
http://creativecommons.org/licenses/by-nc-nd/4.0/
Copyright Statement
This is an open access article under the CCBY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
You might also like
Multiplayer Serious Games Supporting Programming Learning
(2023)
Conference Proceeding
BETTER: An Automatic feedBack systEm for supporTing emoTional spEech tRaining
(2023)
Book Chapter
Hybrid Weighted Retrieval of Twitter Users for Temporally Relevant Full-Text Querying in the Media Industry
(2022)
Conference Proceeding
A Topic-Centric Crowdsourced Assisted Biomedical Literature Review Framework for Academics
(2022)
Conference Proceeding
Downloadable Citations
About Durham Research Online (DRO)
Administrator e-mail: dro.admin@durham.ac.uk
This application uses the following open-source libraries:
SheetJS Community Edition
Apache License Version 2.0 (http://www.apache.org/licenses/)
PDF.js
Apache License Version 2.0 (http://www.apache.org/licenses/)
Font Awesome
SIL OFL 1.1 (http://scripts.sil.org/OFL)
MIT License (http://opensource.org/licenses/mit-license.html)
CC BY 3.0 ( http://creativecommons.org/licenses/by/3.0/)
Powered by Worktribe © 2024
Advanced Search