Foreign Exchange Forecasting Models: LSTM and BiLSTM Comparison †

Abstract

Knowledge of foreign exchange rates and their evolution is fundamental to firms and investors, both for hedging exchange rate risk and for investment and trading. The ARIMA model has been one of the most widely used methodologies for time series forecasting. Nowadays, neural networks have surpassed this methodology in many aspects. For short-term stock price prediction, neural networks in general and recurrent neural networks such as the long short-term memory (LSTM) network in particular perform better than classical econometric models. This study presents a comparative analysis between the LSTM model and BiLSTM models. There is evidence for an improvement in the bidirectional model for predicting foreign exchange rates. In this case, we analyse whether this efficiency is consistent in predicting different currencies as well as the bitcoin futures contract.