Gold Price Direction Prediction Using Machine Learning with Walk-Forward Validation

Fares Dael

doi:10.35377/saucis...1834952

Gold Price Direction Prediction Using Machine Learning with Walk-Forward Validation

Abstract

This study presents a robust machine learning framework for gold price direction prediction using non-overlapping time-series data and walk-forward validation. Unlike prior studies that report overly optimistic results due to temporal leakage and overlapping samples, this work ensures a realistic evaluation by constructing independent observations and employing a rolling validation strategy. A set of technical indicators, including RSI, MACD, and Bollinger Bands, is used as a set of predictive features. Multiple models, including Logistic Regression, Random Forest, and Support Vector Machines, are evaluated against a persistence-based baseline. Experimental results show that all machine learning models outperform the baseline, achieving an average accuracy of approximately 69% and balanced accuracy above 70%. Additionally, the Random Forest model achieves an AUC score of 0.786, indicating strong discriminative capability. Feature importance analysis highlights the significance of momentum and volatility indicators in predicting market direction. The findings demonstrate that, while Financial markets remain inherently noisy, and carefully designed validation strategies can yield reliable and interpretable predictive performance. This study contributes to the field by providing a realistic and reproducible framework that enhances the reliability of financial forecasting models and supports more informed decision-making in practical investment scenarios.

Keywords

Ethical Statement

It is declared that during the preparation process of this study, scientific and ethical principles were followed, and all the studies benefited from are stated in the bibliography.

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Details

Primary Language

English

Subjects

Artificial Intelligence (Other)

Journal Section

Research Article

Authors

Fares Dael ^*
0000-0002-4546-8382
Türkiye

Publication Date

September 30, 2026

Submission Date

December 2, 2025

Acceptance Date

June 22, 2026

Published in Issue

Year 2026 Volume: 9 Number: 4

DOI

https://doi.org/10.35377/saucis...1834952

IZ

https://izlik.org/JA47HD68XN

Cite

RIS / Bibtex

APA

Dael, F. (2026). Gold Price Direction Prediction Using Machine Learning with Walk-Forward Validation. Sakarya University Journal of Computer and Information Sciences, 9(4), 1209-1219. https://doi.org/10.35377/saucis...1834952

AMA

1.Dael F. Gold Price Direction Prediction Using Machine Learning with Walk-Forward Validation. SAUCIS. 2026;9(4):1209-1219. doi:10.35377/saucis.1834952

Chicago

Dael, Fares. 2026. “Gold Price Direction Prediction Using Machine Learning With Walk-Forward Validation”. Sakarya University Journal of Computer and Information Sciences 9 (4): 1209-19. https://doi.org/10.35377/saucis. 1834952.

EndNote

Dael F (September 1, 2026) Gold Price Direction Prediction Using Machine Learning with Walk-Forward Validation. Sakarya University Journal of Computer and Information Sciences 9 4 1209–1219.

IEEE

[1]F. Dael, “Gold Price Direction Prediction Using Machine Learning with Walk-Forward Validation”, SAUCIS, vol. 9, no. 4, pp. 1209–1219, Sept. 2026, doi: 10.35377/saucis...1834952.

ISNAD

Dael, Fares. “Gold Price Direction Prediction Using Machine Learning With Walk-Forward Validation”. Sakarya University Journal of Computer and Information Sciences 9/4 (September 1, 2026): 1209-1219. https://doi.org/10.35377/saucis. 1834952.

JAMA

1.Dael F. Gold Price Direction Prediction Using Machine Learning with Walk-Forward Validation. SAUCIS. 2026;9:1209–1219.

MLA

Dael, Fares. “Gold Price Direction Prediction Using Machine Learning With Walk-Forward Validation”. Sakarya University Journal of Computer and Information Sciences, vol. 9, no. 4, Sept. 2026, pp. 1209-1, doi:10.35377/saucis. 1834952.

Vancouver

1.Fares Dael. Gold Price Direction Prediction Using Machine Learning with Walk-Forward Validation. SAUCIS. 2026 Sep. 1;9(4):1209-1. doi:10.35377/saucis. 1834952