Sun, Zhongtian, Harit, Anoushka, Cristea, Alexandra I., Wang, Jingyun, Lio, Pietro (2023) Money: Ensemble learning for stock price movement prediction via a convolutional network with adversarial hypergraph model. AI Open, 4 . pp. 165-174. ISSN 2666-6510. (doi:10.1016/j.aiopen.2023.10.002) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:108651)
| The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. (Contact us about this Publication) | |
| Official URL: https://doi.org/10.1016/j.aiopen.2023.10.002 |
Abstract
Highlights
•Introducing MONEY, a novel ensemble learning framework to predict stock price movement, which can capture both group-level and pairwise relations.
•The is the first study to demonstrate the effectiveness of integrating auxiliary information via GNNs before using RNNs for temporal studies.
•Implementing experiments on real-world dataset and significantly outperforms the state-of-the-art with steady performance, particularly under a bear market.
| Item Type: | Article |
|---|---|
| DOI/Identification number: | 10.1016/j.aiopen.2023.10.002 |
| Uncontrolled keywords: | Semi-supervised learning; Graph neural network; Knowledge representation and application |
| Divisions: | Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing |
| Depositing User: | Zhongtian Sun |
| Date Deposited: | 05 Feb 2025 18:10 UTC |
| Last Modified: | 11 Feb 2025 16:07 UTC |
| Resource URI: | https://kar.kent.ac.uk/id/eprint/108651 (The current URI for this page, for reference purposes) |
University of Kent Author Information
Sun, Zhongtian.
| Creator's ORCID: |
 https://orcid.org/0000-0003-0489-5203
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|---|---|
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