Forecasting the USD/SEK exchange rate using deep neural networks

Abstract: This thesis is about predicting the average ten minute closing bid price of the USD/SEK exchange rate by applying deep learning methods. First, the time lag method is applied for the vanilla Feedforward Neural Network (FNN) to undertake one-step prediction. Secondly, three univariate Long Short-Term Memory (LSTM) models are used to undertake one-step and multi-step prediction. Each network is theoretically described and motivated. The results indicate that both the FNN and LSTM are applicable to time series prediction and that the LSTM outperforms the FNN. Furthermore, the results suggests the LSTM can outperform the naïve predictor by a small margin but it remains uncertain. It is concluded that to detect structure in the exchange rate much more computing power might be required to learn from significantly longer time series as input. Finally, some economic theory is reviewed and presented which could be used as potential inputs improving the results. A small discussion on overlooked biases is also included.