Forecasting Exchange Rates by Genetic Algorithms Based Back Propagation Network Model

Forecasting currency exchange rates is an important issue in finance. This topic has received much attention, particularly in econometrics and financial selection of variables that influence forecasts. In this paper, a new forecasting model is constructed: we adopt a Genetic Algorithm (GA) to provide the optimal variables weight and we select the optimal set of variables as the input layer neurons, and then we predict the exchange rates with the Back Propagation Network (BPN), called the GABPN model. Basically, we expect improved variable selection to provide better forecasting performance than a random method. As a result, our experiments showed that the GABPN obtained the best forecasting performance and was highly consistent with the actual data. Within the selected 27 variables, only 10 variables play critical factors in influencing forecasting performance; moreover, the GABPN method with proper variables even outperformed the case with full variables. In addition, the proposed model provides valuable information in financial analysis by providing the correct variables that most influence exchange rate trends.