 Forecasting excess returns of the gold market: Can we learn from stock market predictions?Hubert Dichtl Journal of Commodity Markets, 2020, vol. 19, issue C Abstract: As some recent studies have shown empirically, future gold price fluctuations are especially difficult to forecast. Against this background, this study evaluates the forecasting power of three approaches that have been applied successfully in a stock market prediction context: 1) technical indicators, 2) diffusion indices, and 3) economically motivated restrictions in predictive regressions. The results are evaluated using statistical and economic evaluation criteria over the entire data sample, as well as separately for expansive and recessive business cycles. We observe that none of the three prediction techniques leads to better forecasts of gold excess returns. The forecast power of fundamental predictor variables is not only highly regime-dependent, but also dependent on the selected economic evaluation criterion. Future gold forecast studies should address these issues. Keywords: Gold excess return prediction; Fundamental factors; Technical factors; Diffusion indices; Predictive regression models; Restrictions; Business cycles (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:19:y:2020:i:c:s2405851319300716 DOI: 10.1016/j.jcomm.2019.100106 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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