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Gold Against the Machine

Author

Listed:
  • Vasilios Plakandaras

    (Democritus University of Thrace)

  • Periklis Gogas

    (Democritus University of Thrace)

  • Theophilos Papadimitriou

    (Democritus University of Thrace)

Abstract

Despite the increasing significance and the central role of stock markets, investing in gold has remained a popular choice among market participants. The necessity to forecast gold prices has sparked a voluminous literature on the matter, though there is no consensus regarding the variables that drive gold prices evolution or the methodology that adheres to the true data generating mechanism. In this paper, we forecast gold prices comparing econometric and machine learning methodologies in order to produce a model that can better grasps the dynamics of gold prices. To do so, we filter the most prominent variables proposed by the relevant literature exploiting the ability of the Ensemble Empirical Mode Decomposition algorithm to separate noise from the actual evolution of a timeseries. Then, we train Support Vector Regression models coupled with the linear and nonlinear kernels. Our empirical findings suggest that the proposed model adheres closer to gold price evolution than Ordinary Least Square regression and Least Absolute Shrinkage and Selection Operator models used in the literature, while it can be utilized in shaping profitable portfolios.

Suggested Citation

  • Vasilios Plakandaras & Periklis Gogas & Theophilos Papadimitriou, 2021. "Gold Against the Machine," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 5-28, January.
    • Handle: RePEc:kap:compec:v:57:y:2021:i:1:d:10.1007_s10614-020-10019-z
    DOI: 10.1007/s10614-020-10019-z
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    References listed on IDEAS

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    Cited by:

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    2. Lu, Xinjie & Ma, Feng & Xu, Jin & Zhang, Zehui, 2022. "Oil futures volatility predictability: New evidence based on machine learning models11All the authors contribute to the paper equally," International Review of Financial Analysis, Elsevier, vol. 83(C).

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    More about this item

    Keywords

    Gold prices; Forecasting; Machine learning; Support vector machines;
    All these keywords.

    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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