A new ensemble deep learning approach for exchange rates forecasting and trading. (October 2020) - Sun, Shaolong; Wang, Shouyang; Wei, Yunjie
Abstract: This study proposes a new ensemble deep learning approach called LSTM-B by integrating long-short term memory (LSTM) neural network and bagging ensemble learning strategy in order to obtain accurate results of exchange rates forecasting and to improve profitability of exchange rates trading. Previous research literatures have explored exchange rate forecasts, mainly focusing on the validity of forecasts, nevertheless; the precision is only one aspect of exchange rates forecasts. More important than the forecasting performance is how these ensemble learning approaches such as our proposed LSTM-B ensemble deep learning approach can advise professional trading. We extend our forecasts results to examine potential financial profitability of exchange rates between the US dollars (USD) against other four major currencies, such as GBP, JPY, EUR and CNY. The empirical study indicates the effectiveness of our proposed LSTM-B ensemble deep learning approach, which significantly improved forecasting accuracy and potential trading profitability. The proposed LSTM-B ensemble deep learning approach significantly outperforms some other benchmarks with/without bagging ensemble learning strategy under study by means of the forecast performance and the potential trading profitability.