Predictability of commodity futures returns with machine learning models

Abstract

We use prevailing machine learning models to investigate the predictability of futures returns in 22 commodities with commodity-specific and macroeconomic factors as predictors. Out-of-sample prediction errors for the majority of futures contracts are lowered compared with those obtained by the baseline models of AR(1) and forecast combinations. Using Shapley values to explain feature importance, we identify dominant predictors for each commodity. A long–short portfolio strategy based on monthly light gradient-boosting machine predictions outperforms the benchmark linear models in terms of annual return, Sharpe ratio, and max drawdown.