Forex exchange rate forecasting using deep recurrent neural networks

Alexander Jakob Dautel, Wolfgang Karl Härdle, Stefan Lessmann, Hsin‐Vonn SeowFinTech金融人工智能Top Field

Digital Finance2020-03-27Humboldt-Universität zu Berlin; Singapore Management University; University of Nottingham Malaysia CampusDOI

Citations87

Abstract 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 learningComputer scienceRecurrent neural networkArtificial intelligenceProfitability indexForeign exchange marketExchange rateArtificial neural networkFeedforward neural networkFeed forwardDeep neural networksMachine learning