  In this bachelor thesis, we show how four different machine learning methods
(Long Short-Term Memory, Random Forest, Support Vector Machine Regression, and
k-Nearest Neighbor) perform compared to already successfully applied trading
strategies such as Cross Signal Trading and a conventional statistical time
series model ARMA-GARCH. The aim is to show that machine learning methods
perform better than conventional methods in the crude oil market when used
correctly. A more detailed performance analysis was made, showing the
performance of the different models in different market phases so that the
robustness of individual models in high and low volatility phases could be
examined more closely. For further investigation, these models would also have
to be analyzed in other markets.
