 Gold risk premium estimation with machine learning methodsJuan D. Díaz, Erwin Hansen and Gabriel Cabrera Journal of Commodity Markets, 2023, vol. 31, issue C Abstract: This paper assesses the accuracy of several machine learning models’ predictions of the gold risk premium when using an extensive set of 186 predictors. We perform an out-of-sample evaluation and consider both statistical and portfolio metrics. Our results show that machine learning methods and forecast combinations have a limited ability to outperform the historical mean when predicting the gold risk premium. Slightly better results are obtained when predictors are used individually. More specifically, we find that several technical indicators (moving average and momentum series) have forecasting power during periods of expansion, while several business cycle variables and geopolitical risk variables help predict the gold risk premium during recessions. An economic evaluation accounting for transaction costs shows that investors using machine learning methods to estimate expected returns on gold should anticipate limited portfolio gains. Keywords: Gold; Risk premia; Machine learning; Big data; Combination forecasts; Portfolios (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:jocoma:v:31:y:2023:i:c:s2405851322000502 DOI: 10.1016/j.jcomm.2022.100293 Access Statistics for this article Journal of Commodity Markets is currently edited by Marcel Prokopczuk, Betty Simkins and Sjur Westgaard More articles in Journal of Commodity Markets from Elsevier |
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