Data-driven stock forecasting models based on neural networks: A review

Bao, W; Cao, Y; Yang, Y; Che, H; Huang, J; Wen, S

Data-driven stock forecasting models based on neural networks: A review

Bao, W

Cao, Y Yang, Y Che, H Huang, J Wen, S

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Publisher:: Elsevier
Publication Type:: Journal Article
Citation:: Information Fusion, 2025, 113, pp. 102616
Issue Date:: 2025-01-01

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Field	Value	Language
dc.contributor.author	Bao, W https://orcid.org/0000-0003-3736-4513
dc.contributor.author	Cao, Y
dc.contributor.author	Yang, Y
dc.contributor.author	Che, H
dc.contributor.author	Huang, J
dc.contributor.author	Wen, S https://orcid.org/0000-0001-8077-7001
dc.date.accessioned	2024-10-03T13:04:08Z
dc.date.available	2024-10-03T13:04:08Z
dc.date.issued	2025-01-01
dc.identifier.citation	Information Fusion, 2025, 113, pp. 102616
dc.identifier.issn	1566-2535
dc.identifier.uri	http://hdl.handle.net/10453/181175
dc.description.abstract	As a core branch of financial forecasting, stock forecasting plays a crucial role for financial analysts, investors, and policymakers in managing risks and optimizing investment strategies, significantly enhancing the efficiency and effectiveness of economic decision-making. With the rapid development of information technology and computer science, data-driven neural network technologies have increasingly become the mainstream method for stock forecasting. Although recent review studies have provided a basic introduction to deep learning methods, they still lack detailed discussion on network architecture design and innovative details. Additionally, the latest research on emerging large language models and neural network structures has yet to be included in existing review literature. In light of this, this paper comprehensively reviews the literature on data-driven neural networks in the field of stock forecasting from 2015 to 2023, discussing various classic and innovative neural network structures, including Recurrent Neural Networks (RNNs), Convolutional Neural Networks (CNNs), Transformers, Graph Neural Networks (GNNs), Generative Adversarial Networks (GANs), and Large Language Models (LLMs). It analyzes the application and achievements of these models in stock market forecasting. Moreover, the article also outlines the commonly used datasets and various evaluation metrics in the field of stock forecasting, further exploring unresolved issues and potential future research directions, aiming to provide clear guidance and reference for researchers in stock forecasting.
dc.language	en
dc.publisher	Elsevier
dc.relation.ispartof	Information Fusion
dc.relation.isbasedon	10.1016/j.inffus.2024.102616
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0801 Artificial Intelligence and Image Processing
dc.subject.classification	Artificial Intelligence & Image Processing
dc.subject.classification	4602 Artificial intelligence
dc.subject.classification	4603 Computer vision and multimedia computation
dc.subject.classification	4605 Data management and data science
dc.title	Data-driven stock forecasting models based on neural networks: A review
dc.type	Journal Article
utslib.citation.volume	113
utslib.for	0801 Artificial Intelligence and Image Processing
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	University of Technology Sydney/UTS Groups
pubs.organisational-group	University of Technology Sydney/UTS Groups/Australian Artificial Intelligence Institute (AAII)
utslib.copyright.status	open_access	*
dc.rights.license	This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). To view a copy of this license, visit https://creativecommons.org/licenses/by/4.0/
dc.date.updated	2024-10-03T13:04:05Z
pubs.publication-status	Accepted
pubs.volume	113

Abstract:

As a core branch of financial forecasting, stock forecasting plays a crucial role for financial analysts, investors, and policymakers in managing risks and optimizing investment strategies, significantly enhancing the efficiency and effectiveness of economic decision-making. With the rapid development of information technology and computer science, data-driven neural network technologies have increasingly become the mainstream method for stock forecasting. Although recent review studies have provided a basic introduction to deep learning methods, they still lack detailed discussion on network architecture design and innovative details. Additionally, the latest research on emerging large language models and neural network structures has yet to be included in existing review literature. In light of this, this paper comprehensively reviews the literature on data-driven neural networks in the field of stock forecasting from 2015 to 2023, discussing various classic and innovative neural network structures, including Recurrent Neural Networks (RNNs), Convolutional Neural Networks (CNNs), Transformers, Graph Neural Networks (GNNs), Generative Adversarial Networks (GANs), and Large Language Models (LLMs). It analyzes the application and achievements of these models in stock market forecasting. Moreover, the article also outlines the commonly used datasets and various evaluation metrics in the field of stock forecasting, further exploring unresolved issues and potential future research directions, aiming to provide clear guidance and reference for researchers in stock forecasting.

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http://hdl.handle.net/10453/181175