Recurrent neural networks for financial asset forecasting

Sammanfattning: The application of neural networks in finance has found renewed interest in the past few years. Neural networks have a proven capability of modeling non-linear relationships and have been proven widely successful in domains such as image and speech recognition. These favorable properties of the Neural Network make them an alluring choice of model when studying the financial markets. This thesis is concerned with investigating the use of recurrent neural networks for predicting future financial asset price movements on a set of futures contracts. To aid our research, we compare them to a set of simple feed-forward networks. We conduct further research into the various networks by considering different objective loss functions and how they affect our networks performance. This discussion is extended by considering multi-loss networks as well. The use of different loss functions sheds light on the importance of feature selection. We study a set of simple and complex features and how they affect our model. This aids us in further examining the difference between our networks. Lastly, we analyze of the gradients of our model to provide additional insight into the properties of our features. Our results show that recurrent networks provide superior predictive performance compared to feed-forward networks both when evaluating the Sharpe ratio and accuracy. The simple features show better results when optimizing for accuracy. When the network aims to maximize Sharpe, the complex features are preferred. The use of multi-loss networks proved successful when we consider achieving a high Sharpe ratio as our main objective. Our results show significant improved performance compared to a set of simple benchmarks. Through ensemble methods, we achieve a Sharpe ratio of 1.44 and an accuracy of 52.77% on the test set