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 (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.
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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
Institutional Unit:	Schools > School of Computing
Former Institutional Unit:	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:	22 Jul 2025 09:22 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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