Using a hybrid neural network to predict the NTD/USD exchange rate

Abstract

In the financial market, although foreign exchange options or foreign exchange forward contracts are available for corporations to hedge risks, reports of profit losses due to foreign exchange losses remain common. This study employs a multilayer perceptions (MLP) neural network with genetic algorithm (GA) to predict the New Taiwan dollar (NTD)/U.S. dollar (USD) exchange rate. The GA is used to determine the optimum number of input and hidden nodes for a feedforward neural network, the optimum slope of the activation function, and the optimum learning rates and momentum coefficients. The empirical results show that the ability of the proposed model to predict the NTD/USD exchange rate is excellent. The absolute relative error between the predicted value and the actual value was 0.338%, and the correlation coefficient was 0.995885. © 2013 Springer-Verlag.