Gold Against the Machine

Vasilios Plakandaras, Periklis Gogas and Theophilos Papadimitriou

Computational Economics, 2021, vol. 57, issue 1, No 2, 5-28

Abstract: 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.

Keywords: Gold prices; Forecasting; Machine learning; Support vector machines (search for similar items in EconPapers)
JEL-codes: C45 C63 G17 (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s10614-020-10019-z

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