  This study analyses oil price movements through the lens of an agnostic
random forest model, which is based on 1,000 regression trees. It shows that
this highly disciplined, yet flexible computational model reduces in sample
root mean square errors by 65% relative to a standard linear least square model
that uses the same set of 11 explanatory factors. In forecasting exercises the
RMSE reduction ranges between 51% and 68%, 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. If Covid 19 is also
considered as a risk factor, these shares become even larger.
