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Forecasting excess returns of the gold market: Can we learn from stock market predictions?

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  • Dichtl, Hubert

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.

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  • Dichtl, Hubert, 2020. "Forecasting excess returns of the gold market: Can we learn from stock market predictions?," Journal of Commodity Markets, Elsevier, vol. 19(C).
  • Handle: RePEc:eee:jocoma:v:19:y:2020:i:c:s2405851319300716
    DOI: 10.1016/j.jcomm.2019.100106
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    More about this item

    Keywords

    Gold excess return prediction; Fundamental factors; Technical factors; Diffusion indices; Predictive regression models; Restrictions; Business cycles;
    All these keywords.

    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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