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

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 indicator...

Full description

Saved in:
Bibliographic Details
Published in:Journal of computational design and engineering Vol. 10; no. 3; pp. 1110 - 1125
Main Authors: Mousapour Mamoudan, Mobina, Ostadi, Ali, Pourkhodabakhsh, Nima, Fathollahi-Fard, Amir M, Soleimani, Faezeh
Format: Journal Article
Language:English
Published: Oxford University Press 01-06-2023
한국CDE학회
Subjects:
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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 10 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. Graphical Abstract Graphical Abstract
ISSN:2288-5048
2288-4300
2288-5048
DOI:10.1093/jcde/qwad039