Deep learning-based forecasting of financial assets

Sammanfattning: Deep learning and neural networks has recently become a powerful tool to solve complex problem due to improvements in training algorithms. Examples of successful application can be found in speech recognition and machine translation. There exist relative few finance articles were deep learning have been applied, but existing articles indicate that deep learning can be successfully applied to problems in finance.  This thesis studies forecasting of financial price movements using two types of neural networks, namely; feedforward and recurrent networks. For the feedforward neural networks we considered non-deep networks with more neurons and deep networks with fewer neurons. In addition to the comparison between feedforward and recurrent networks, a comparison between deep and non-deep networks will be made. The recurrent architecture consists of a recurrent layer mapping into a feedforward layer followed by an output layer. The networks are trained with two different feature setups, one less complex and one more complex. The findings for non-deep vs. deep feedforward neural networks imply that there does not exist any general pattern whether deep or non-deep networks are preferable. The findings for recurrent neural networks vs. feedforward neural networks imply that recurrent neural networks do not necessarily outperform feedforward neural networks even though financial data in general are time-dependent. In some cases, adding batch normalization can improve the accuracy for the feedforward neural networks. This can be preferable instead of using more complex models, such as a recurrent neural networks. Moreover, there are significant differences in accuracies between using the two different feature setups. The highest accuracy for all networks are 52.82%, which is significantly better than the simple benchmark.