Humboldt-Universität zu Berlin - High Dimensional Nonstationary Time Series

IRTG1792DP2020 006

Forex exchange rate forecasting using deep recurrent neural networks

Alexander Jakob Dautel
Wolfgang Karl Härdle
Stefan Lessmann
Hsin‐Vonn Seow

Deep learning has substantially advanced the state of the art in computer
vision, natural language processing, and other fields. The paper examines the
potential of deep learning for exchange rate forecasting. We systematically
compare long short- term memory networks and gated recurrent units to
traditional recurrent network architectures as well as feedforward networks in
terms of their directional forecasting accuracy and the profitability of trading
model predictions. Empirical results indicate the suitability of deep networks
for exchange rate forecasting in general but also evidence the difficulty of
implementing and tuning corresponding architectures. Especially with regard to
trading profit, a simpler neural network may perform as well as if not better
than a more complex deep neural network.

Deep learning, Financial time series forecasting, Recurrent neural networks,
Foreign exchange rates

JEL Classification:
C14, C22, C45