A Framework for Gold Price Prediction Combining Classical and Intelligent Methods with Financial, Economic, and Sentiment Data Fusion

Gergana Taneva-Angelova, Stefan Raychev and Galina Ilieva ()
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Gergana Taneva-Angelova: Department of Finance and Accounting, University of Plovdiv Paisii Hilendarski, 4000 Plovdiv, Bulgaria
Stefan Raychev: Department of Economic Theories, University of Plovdiv Paisii Hilendarski, 4000 Plovdiv, Bulgaria
Galina Ilieva: Department of Management and Quantitative Methods in Economics, University of Plovdiv Paisii Hilendarski, 4000 Plovdiv, Bulgaria

IJFS, 2025, vol. 13, issue 2, 1-25

Abstract: Accurate gold price forecasting is essential for informed financial decision-making, as gold is sensitive to economic, political, and social factors. This study presents a hybrid framework for multivariate gold price prediction that integrates classical econometric modelling, traditional machine learning, modern deep learning methods, and their combinations. The framework incorporates financial, macroeconomic, and sentiment indicators, allowing it to capture complex temporal patterns and cross-variable relationships over time. Empirical validation on an eleven-year dataset (2014–2024) demonstrates the framework effectiveness across diverse market conditions. Results show that advanced supervised techniques outperform traditional econometric models under dynamic market environment. Key advantages of the framework include its ability to handle multiple data types, apply a structured variable selection process, employ diverse model families, and support model hybridisation and meta-modelling, providing practical guidance for investors, institutions, and policymakers.

Keywords: gold price prediction; gold futures forecasting; financial time series; machine learning; deep learning; macroeconomic indicators; financial indicators; sentiment analysis; forecasting accuracy (search for similar items in EconPapers)
JEL-codes: F2 F3 F41 F42 G1 G2 G3 (search for similar items in EconPapers)
Date: 2025
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