Abstract

Accurately forecasting gold price actions is critical in financial markets due to gold's role as a safe-haven asset. This paper addresses the challenge of forecasting gold prices by applying a Genetic Algorithm (GA) with a Multi-Layer Perceptron (MLP) model. The research exploits historical financial data from various instruments, including gold futures, Bitcoin, and key currency pairs, to improve prediction accuracy. By optimizing the MLP's hyper parameters through GA, the model efficiently captures complex relationships in high-frequency data, achieving a remarkable R² score of 0.9993. This level of precision demonstrates the model's potential for providing actionable insights for traders and investors. The study's conclusions highlight the feasibility of combining machine learning techniques with evolutionary algorithms in financial forecasting, offering a valuable tool for navigating market volatility.

Department(s)

Materials Science and Engineering

Publication Status

Open Access

Keywords and Phrases

Financial forecasting; Genetic algorithms; Gold Price forecastings; High-frequency trading; Hyper-parameter optimization; Machine learning; Multi-layer perceptron

Subject Headings

Gold sales & prices; Business forecastings; Gold futures; Evolutionary algorithms; Genetic algorithms

International Standard Serial Number (ISSN)

2369-0739

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2026 The Authors, All rights reserved

Creative Commons Licensing

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.

Publication Date

2024-12-31

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