Hybrid neural network-based metaheuristics for prediction of financial markets: a case study on global gold market

IF 4.8 2区 工程技术 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Journal of Computational Design and Engineering Pub Date : 2023-05-02 DOI:10.1093/jcde/qwad039
Mobina Mousapour Mamoudan, A. Ostadi, Nima Pourkhodabakhsh, A. M. F. Fard, Faezeh Soleimani
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引用次数: 4

Abstract

Technical analysis indicators are popular tools in financial markets. These tools help investors to identify buy and sell signals with relatively large errors. The main goal of this study is to develop new practical methods to identify fake signals obtained from technical analysis indicators in the precious metals market. In this paper, we analyze these indicators in different ways based on the recorded signals for ten months. The main novelty of this research is to propose hybrid neural network-based metaheuristic algorithms for analyzing them accurately while increasing the performance of the signals obtained from technical analysis indicators. We combine a convolutional neural network and a bidirectional gated recurrent unit whose hyperparameters are optimized using the firefly metaheuristic algorithm. To determine and select the most influential variables on the target variable, we use another successful recently-developed metaheuristic, namely, the moth-flame optimization algorithm. Finally, we compare the performance of the proposed models with other state-of-the-art single and hybrid deep learning and machine learning methods from the literature. Finally, the main finding is that the proposed neural network-based metaheuristics can be useful as a decision support tool for investors to address and control the enormous uncertainties in the financial and precious metals markets.
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基于混合神经网络的金融市场元启发式预测——以全球黄金市场为例
技术分析指标是金融市场上常用的工具。这些工具帮助投资者识别误差相对较大的买入和卖出信号。本研究的主要目的是开发新的实用方法来识别贵金属市场中从技术分析指标中获得的假信号。本文根据10个月的记录信号,对这些指标进行了不同的分析。本研究的主要新颖之处在于提出了基于混合神经网络的元启发式算法来准确地分析它们,同时提高了从技术分析指标获得的信号的性能。我们结合了一个卷积神经网络和一个双向门控循环单元,其超参数使用萤火虫元启发式算法进行优化。为了确定和选择对目标变量影响最大的变量,我们使用了最近成功开发的另一种元启发式算法,即蛾焰优化算法。最后,我们将所提出模型的性能与文献中其他最先进的单一和混合深度学习和机器学习方法进行了比较。最后,本文的主要发现是,提出的基于神经网络的元启发式方法可以作为一种决策支持工具,帮助投资者解决和控制金融和贵金属市场中的巨大不确定性。
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来源期刊
Journal of Computational Design and Engineering
Journal of Computational Design and Engineering Computer Science-Human-Computer Interaction
CiteScore
7.70
自引率
20.40%
发文量
125
期刊介绍: Journal of Computational Design and Engineering is an international journal that aims to provide academia and industry with a venue for rapid publication of research papers reporting innovative computational methods and applications to achieve a major breakthrough, practical improvements, and bold new research directions within a wide range of design and engineering: • Theory and its progress in computational advancement for design and engineering • Development of computational framework to support large scale design and engineering • Interaction issues among human, designed artifacts, and systems • Knowledge-intensive technologies for intelligent and sustainable systems • Emerging technology and convergence of technology fields presented with convincing design examples • Educational issues for academia, practitioners, and future generation • Proposal on new research directions as well as survey and retrospectives on mature field.
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