Prior research has shown that it is possible to include sentiment data in, for example, GARCH or VAR(p) models and thereby increase model accuracy. This projects key question is how these sentiment augmented models perform in comparison to machine learning approaches like neural networks. Therefore, the traditional sentiment augmented models are seen as benchmarks and different ML approaches will be tried to see if the accuracy can be increased.