Humboldt-Universität zu Berlin - High Dimensional Nonstationary Time Series

IRTG1792DP2019 008

Forex Exchange Rate Forecasting Using Deep Recurrent Neural Networks

Alexander J. Dautel
Wolfgang K. Härdle
Stefan Lessmann
Hsin-Vonn Seow

Deep learning has substantially advanced the state-of-the-art in computer
vision, natural language processing and other elds. The paper examines the
potential of contemporary recurrent deep learning architectures for nancial
time series forecasting. Considering the foreign exchange market as testbed, we
systematically compare long short-term memory networks and gated recurrent units
to traditional recurrent 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 diculty of
implementing and tuning corresponding architectures. Especially with regard to
trading pro t, 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: