Application of grey relational analysis and artificial neural networks on currency exchange-traded notes (ETNs)

Abstract

Abstract This study determines which index has the strongest influence on the exchange-trade note (ETN) returns using the grey relational analysis. Results show that the volatility index is the strongest, followed by the S&P 500 stock index, the US dollar index, the CRB index, the Trade index, and the Brent crude oil index. However, the US dollar index has the most significant effect of using the index values of currency ETNs, followed by the S&P 500 stock index, volatility index, Brent crude oil index, the CRB index, and Trade index. This study applies four types of the artificial neural network model, namely, back-propagation neural network (BPN), recurrent neural network (RNN), time-delay recurrent neural network (TDRNN), and radial basis function neural network (RBFNN) to capture the nonlinear tendencies of ETNs for better forecasting accuracy. The paper finds that the RNN and RBFNN models have stronger predictive power among the models, and provides the highest forecasting accuracy for the majority of the currency ETNs. However, the RNN model consistently shows that the low grey relational grades (GRG) variables have the strongest influence on the ETN returns, compared with combining all and high GRG variables. These findings suggest that fund managers and traders can potentially rely on both RNN and RBFNN models, particularly the former, in their applications in financial time-series modeling.