A combined Independent Component Analysis–Neural Network model for forecasting exchange rate variation

Abstract

The currency market is one of the most efficient markets, making it very difficult to predict future prices. Several studies have sought to develop more accurate models to predict the future exchange rate by analyzing econometric models, developing artificial intelligence models and combining both through the creation of hybrid models. This paper proposes a hybrid model for forecasting the variations of five exchange rates related to the US Dollar: Euro, British Pound, Japanese Yen, Swiss Franc and Canadian Dollar. The proposed model uses Independent Component Analysis (ICA) to deconstruct the series into independent components as well as neural networks (NN) to predict each component. This method differentiates this study from previous works where ICA has been used to extract the noise of time series or used to obtain explanatory variables that are then used in forecasting. The proposed model is then compared to random walk, autoregressive and conditional variance models, neural networks, recurrent neural networks and long–short term memory neural networks. The hypothesis of this study supposes that first deconstructing the exchange rate series and then predicting it separately would produce better forecasts than traditional models. By using the mean squared error and mean absolute percentage error as a measures of performance and Model Confidence Sets to statistically test the superiority of the proposed model, our results showed that this model outperformed the other models examined and significantly improved the accuracy of forecasts. These findings support this model’s use in future research and in decision-making related to investments.