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

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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)

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.

Suggested Citation

  • Gergana Taneva-Angelova & Stefan Raychev & Galina Ilieva, 2025. "A Framework for Gold Price Prediction Combining Classical and Intelligent Methods with Financial, Economic, and Sentiment Data Fusion," IJFS, MDPI, vol. 13(2), pages 1-25, June.
    • Handle: RePEc:gam:jijfss:v:13:y:2025:i:2:p:102-:d:1671850
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    References listed on IDEAS

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    3. Fang, Libing & Yu, Honghai & Xiao, Wen, 2018. "Forecasting gold futures market volatility using macroeconomic variables in the United States," Economic Modelling, Elsevier, vol. 72(C), pages 249-259.
    4. Fischer, Thomas & Krauss, Christopher, 2018. "Deep learning with long short-term memory networks for financial market predictions," European Journal of Operational Research, Elsevier, vol. 270(2), pages 654-669.
    5. Ayodele Ariyo Adebiyi & Aderemi Oluyinka Adewumi & Charles Korede Ayo, 2014. "Comparison of ARIMA and Artificial Neural Networks Models for Stock Price Prediction," Journal of Applied Mathematics, Hindawi, vol. 2014, pages 1-7, March.
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