Predictability of commodity futures returns with machine learning models
用机器学习模型研究22种大宗商品期货收益的可预测性，发现多数合约预测误差低于传统模型，基于LightGBM的多空策略在年化收益、夏普比率和最大回撤上优于线性基准。
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.