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- W3163986743 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 feed-forward 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." @default.
- W3163986743 created "2021-06-07" @default.
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- W3163986743 date "2020-01-01" @default.
- W3163986743 modified "2023-09-26" @default.
- W3163986743 title "Forex Exchange Rate Forecasting Using Deep Recurrent Neural Networks" @default.
- W3163986743 hasPublicationYear "2020" @default.
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