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.

本文评估了多种机器学习模型在使用大量 186 个预测变量时预测黄金风险溢价的准确性。我们进行样本外评估并考虑统计和投资组合指标。我们的结果表明，机器学习方法和预测组合在预测黄金风险溢价时超越历史平均值的能力有限。单独使用预测变量时可以获得稍微更好的结果。更具体地说，我们发现一些技术指标（移动平均线和动量序列）在扩张时期具有预测能力，而一些经济周期变量和地缘政治风险变量有助于预测经济衰退期间的黄金风险溢价。考虑到交易成本的经济评估表明，使用机器学习方法来估计黄金预期回报的投资者应该预计投资组合收益有限。