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DC Field | Value | Language |
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dc.contributor.author | Liu, R | - |
dc.contributor.author | Liu, H | - |
dc.contributor.author | Huang, H | - |
dc.contributor.author | Song, B | - |
dc.contributor.author | Wu, Q | - |
dc.date.accessioned | 2023-12-23T12:56:10Z | - |
dc.date.available | 2023-12-23T12:56:10Z | - |
dc.date.issued | 2023-12-21 | - |
dc.identifier | ORCID iD: Ruirui Liu ... | - |
dc.identifier | ORCID iD: Haoxian Liu https://orcid.org/0000-0001-7735-7700 | - |
dc.identifier | ORCID iD: Qingyao Wu https://orcid.org/0000-0002-8564-7289 | - |
dc.identifier | 110211 | - |
dc.identifier.citation | Liu, R. et al. (2024) 'Multimodal multiscale dynamic graph convolution networks for stock price prediction', Pattern Recognition, 149 (May 2024), 110211, pp. 1 - 14. doi: 10.1016/j.patcog.2023.110211. | en_US |
dc.identifier.issn | 0031-3203 | - |
dc.identifier.uri | https://bura.brunel.ac.uk/handle/2438/27918 | - |
dc.description | Data Availability: Data will be made available on request. | en_US |
dc.description.abstract | Predicting directional future stock price movements is very challenging due to the complex, stochastic, and evolving nature of the financial markets. Existing literature either neglects other timely and granular alternative data, such as media text data, or fails to extract and distill predictive multimodal features from the data. Moreover, the time-varying cross-sectional relations beyond sequential dependencies of stock prices are informative for forecasting price fluctuations, for which the modelling flexibility, however, is not adequate in most of the previous studies. In this paper, we propose a novel Multiscale Multimodal Dynamic Graph Convolution Network (Melody-GCN) to address these issues in stock price prediction. It contains three core modules: (1) multimodal fusing-diffusing blocks that effectively integrate and align the numerical and textual features; (2) a multiscale architecture that extracts and refines temporal features via a fine-to-coarse descending path and a coarse-to-fine ascending path progressively; and (3) dynamic spatio-temporal graph convolutional layers that learn the complex and evolving stock relations not only in between industries and individual companies but also across time horizons. Extensive experimental results and trading simulations on two real-world datasets demonstrate the superior performance of our proposed approach beyond other state-of-the-art models. | en_US |
dc.description.sponsorship | National Natural Science Foundation of China (NSFC) 61876208 and 62272172; Tip-top Scientific and Technical Innovative Youth Talents of Guangdong Special Support Program (2019TQ05X200); 2022 Tencent WeChat Rhino-Bird Focused Research Program Research (Tencent WeChat RBFR2022008); Ruirui Liu also thanks the financial grant on big-data analytics and machine learning from the Qatar Centre for Global Banking and Finance of King's Business School, King's College London. | en_US |
dc.format.extent | 1 - 14 | - |
dc.format.medium | Print-Electronic | - |
dc.language | English | - |
dc.language.iso | en | en_US |
dc.publisher | Elsevier | en_US |
dc.rights | Copyright © Elsevier 2023. All rights reserved. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/ | - |
dc.rights.uri | https://creativecommons.org/licenses/by-nc-nd/4.0/ | - |
dc.subject | stock movement prediction | en_US |
dc.subject | multimodal feature fusing | en_US |
dc.subject | multiscale architecture | en_US |
dc.subject | graph convolutional network | en_US |
dc.title | Multimodal multiscale dynamic graph convolution networks for stock price prediction | en_US |
dc.type | Article | en_US |
dc.identifier.doi | https://doi.org/10.1016/j.patcog.2023.110211 | - |
dc.relation.isPartOf | Pattern Recognition | - |
pubs.publication-status | Published | - |
pubs.volume | 149 | - |
dc.identifier.eissn | 1873-5142 | - |
dc.rights.holder | Elsevier | - |
Appears in Collections: | Dept of Economics and Finance Embargoed Research Papers |
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FullText.pdf | Embargoed until 21 December 2024 | 6.16 MB | Adobe PDF | View/Open |
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