Classification of Exchange Rate Prediction Methods with a Focus on Deep Learning Techniques

Currency exchange rate forecasting has always been one of the important issues for economic activists. In this context, the stationary, and non-linear behavior of this variable and random walk claim mentioned in some empirical studies have made forecasting as one of the challenges, and concerns in the field of economics. The present study briefly classifies various currency exchange rates forecasting models and methods, then focuses on five deep learning methods, including Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU) and Reinforcement Learning (RL). For this purpose, the report of various studies on forecasting currency exchange rates using the above methods, with objectives, such as identifying the researchers in this field, the scope of studies, the scientific centers conducting studies, along with their geographical distribution, mixed methods used, studied currency pairs, forecasting periods, data frequency, evaluation criteria, obtained accuracy and features used for forecasting were studied. The study results will help future research in this field more effectively identify the research gaps with classified access to the previous studies, and define the topic and scope of future research to complete previous studies.