A random walk through the trees: Forecasting copper prices using decision learning methods

We investigate the accuracy of copper price forecasts produced by three decision learning methods. Prior evidence (Liu et al. Resources Policy, 2017) shows that a regression tree, a simple decision learning model, can be used to predict copper prices for both short-term and long-term horizons (several days and several years, respectively). We contribute to this literature by evaluating more sophisticated decision learning methods based on trees: random forests and gradient boosting regression trees. Our results indicate that random forests and gradient boosting regression trees significantly outperform regression trees at forecasting copper prices. Our analysis also reveals that a random walk process, recognized in the literature as one of the most useful models for forecasting copper prices, yields competitive out-of-sample forecasts as compared to these decision learning methods. •We forecast copper prices using tree-based decision learning methods.•A random forest and gradient boosting models outperform the regression tree model.•In the short- and medium-run, the Random Walk model produces the best forecasts.•For more distant horizons (2 years), decision learning models are more competitive.