Forecasting oil prices with random forests.

This study analyzes oil price movements through the lens of an agnostic random forest model, which is based on 1000 regression trees. It shows that this highly disciplined, yet flexible computational model reduces in-sample root-mean-square errors (RMSEs) by 65% relative to a standard linear least square model that uses the same comprehensive set of 11 high-frequency explanatory factors. In 1–3 months ahead price forecasting exercises the RMSE reduction relative to OLS ranges exceeds 50%, highlighting the relevance of non-linearities in oil markets. The results underscore the importance of incorporating financial factors into oil models: US interest rates, the dollar and the VIX together account for 39% of the models' RMSE reduction in the post-2010 sample, rising to 48% in the post-2020 sample. [ABSTRACT FROM AUTHOR]