Multi-Horizon Gold Price Forecasting and Its Implications for
Financial Markets
Asifa Iqbal 1,*, Marwa M. Eid2,3
1School of international languages Zhengzhou University, Henan, China
2Faculty of Artificial Intelligence, Delta University for Science and Technology, Mansoura 11152, Egypt
3Jadara Research Center, Jadara University, Irbid 21110, Jordan
Emails: asifaiqbal615@gmail.com; mmm@ieee.org
Abstract
Accurate forecasting of gold prices remains a critical challenge in financial markets due to the nonlinear,
nonstationary, and regime-dependent nature of commodity price dynamics, particularly for gold quoted against
the US dollar (XAU/USD), which plays a central role as a safe-haven asset, inflation hedge, and portfolio
diversifier. Motivated by the growing limitations of traditional econometric and manually tuned machine
learning approaches in handling long-horizon, multi-timeframe financial data, this study proposes a robust
forecasting framework that integrates deep learning with metaheuristic optimization. The main contribution
of this work lies in the systematic combination of a Deep Pyramid Recurrent Neural Network (DPRNN) with
advanced metaheuristic algorithms for automated hyperparameter optimization, with particular emphasis on
Greylag Goose Optimization (GGO), alongside other state-of-the-art optimizers. Using historical XAU/USD
data spanning from 2004 to February 2025 across multiple temporal resolutions, baseline model evaluation
demonstrates that DPRNN outperforms other deep learning architectures prior to optimization, achieving
a Mean Squared Error (MSE) of 0.0589, Root Mean Squared Error (RMSE) of 0.2426, and coefficient of
determination (R2) of 0.79. Following optimization, the proposed GGO-optimized DPRNN framework yields
a substantial performance enhancement, reducing the MSE to 2.05 × 10−5 and RMSE to 4.52 × 10−3, while
simultaneously increasing the correlation coefficient to 0.987 and R2 to 0.983, with near-perfect agreement
metrics reflected by a Nash–Sutcliffe Efficiency of 0.986 and Willmott Index of 0.988. These results confirm
the effectiveness of GGO in navigating complex hyperparameter search spaces and significantly improving
predictive accuracy and stability. From an economic and financial perspective, the findings underscore
the practical value of metaheuristic-optimized deep learning models for enhancing gold price forecasting,
supporting more informed investment decisions, improved risk management, and greater market efficiency in
volatile and uncertain financial environments.
Keywords: Gold price forecasting; Financial time-series modeling; Safe-haven assets; Metaheuristic
optimization; Deep learning in financial markets