Gold Price Prediction Through Fusion of Financial, Economic and Sentiment Factors

Accurate prediction of gold prices is essential for informed financial decision-making, given their sensitivity to economic, political, and social factors. This study proposes a new hybrid framework for forecasting gold prices, combining traditional financial modelling, classical machine learning, and advanced deep learning methods, including long short-term memory networks and their variations. The framework integrates financial, macroeconomic, and sentiment indicators through feature fusion, capturing complex temporal dynamics and cross‑variable dependencies to improve prediction accuracy. The experimental evaluation spans a ten-year period (2014–2024), allowing for a robust assessment of framework performance in forecasting gold futures under varying market conditions. Comparative analysis of classical econometric and modern machine‑learning models shows that advanced methods achieve higher forecasting accuracy and remain robust despite data variability.