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GCN-based stock relations analysis for stock market prediction
Most stock price predictive models merely rely on the target stock’s historical information to forecast future prices, where the linkage effects between stocks are neglected. However, a group of prior studies has shown that the leverage of correlations between stocks could significantly improve the...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
PeerJ Inc.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9455286/ https://www.ncbi.nlm.nih.gov/pubmed/36092004 http://dx.doi.org/10.7717/peerj-cs.1057 |
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author | Zhao, Cheng Liu, Xiaohui Zhou, Jie Cen, Yuefeng Yao, Xiaomin |
author_facet | Zhao, Cheng Liu, Xiaohui Zhou, Jie Cen, Yuefeng Yao, Xiaomin |
author_sort | Zhao, Cheng |
collection | PubMed |
description | Most stock price predictive models merely rely on the target stock’s historical information to forecast future prices, where the linkage effects between stocks are neglected. However, a group of prior studies has shown that the leverage of correlations between stocks could significantly improve the predictions. This article proposes a unified time-series relational multi-factor model (TRMF), which composes a self-generating relations (SGR) algorithm that can extract relational features automatically. In addition, the TRMF model integrates stock relations with other multiple dimensional features for the price prediction compared to extant works. Experimental validations are performed on the NYSE and NASDAQ data, where the model is compared with the popular methods such as attention Long Short-Term Memory network (Attn-LSTM), Support Vector Regression (SVR), and multi-factor framework (MF). Results show that compared with these extant methods, our model has a higher expected cumulative return rate and a lower risk of return volatility. |
format | Online Article Text |
id | pubmed-9455286 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | PeerJ Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-94552862022-09-09 GCN-based stock relations analysis for stock market prediction Zhao, Cheng Liu, Xiaohui Zhou, Jie Cen, Yuefeng Yao, Xiaomin PeerJ Comput Sci Artificial Intelligence Most stock price predictive models merely rely on the target stock’s historical information to forecast future prices, where the linkage effects between stocks are neglected. However, a group of prior studies has shown that the leverage of correlations between stocks could significantly improve the predictions. This article proposes a unified time-series relational multi-factor model (TRMF), which composes a self-generating relations (SGR) algorithm that can extract relational features automatically. In addition, the TRMF model integrates stock relations with other multiple dimensional features for the price prediction compared to extant works. Experimental validations are performed on the NYSE and NASDAQ data, where the model is compared with the popular methods such as attention Long Short-Term Memory network (Attn-LSTM), Support Vector Regression (SVR), and multi-factor framework (MF). Results show that compared with these extant methods, our model has a higher expected cumulative return rate and a lower risk of return volatility. PeerJ Inc. 2022-08-11 /pmc/articles/PMC9455286/ /pubmed/36092004 http://dx.doi.org/10.7717/peerj-cs.1057 Text en ©2022 Zhao et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited. |
spellingShingle | Artificial Intelligence Zhao, Cheng Liu, Xiaohui Zhou, Jie Cen, Yuefeng Yao, Xiaomin GCN-based stock relations analysis for stock market prediction |
title | GCN-based stock relations analysis for stock market prediction |
title_full | GCN-based stock relations analysis for stock market prediction |
title_fullStr | GCN-based stock relations analysis for stock market prediction |
title_full_unstemmed | GCN-based stock relations analysis for stock market prediction |
title_short | GCN-based stock relations analysis for stock market prediction |
title_sort | gcn-based stock relations analysis for stock market prediction |
topic | Artificial Intelligence |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9455286/ https://www.ncbi.nlm.nih.gov/pubmed/36092004 http://dx.doi.org/10.7717/peerj-cs.1057 |
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