The forecasting of consumer exchange-traded funds (ETFs) via grey relational analysis (GRA) and artificial neural network (ANN)

Abstract

Our study uses the grey relational analysis (GRA) and artificial neural network (ANN) models for the prediction of consumer exchange-traded funds (ETFs). We apply eight variables, including the put/call ratio, the EUR/USD exchange rate, the volatility index, the Commodity Research Bureau Index (CRB), the short-term trading index, the New York Stock Exchange Composite Index, inflation, and the interest rate. The GRA model results showed that the NYSE, CRB, EUR/USD, and PCR were the four main variables influencing consumer ETFs. The GRA test results of all the ANN models' data showed that the back propagation neural network (BPN) was the best predictive model. Based on the classification of different percentages of training data, the results of GRA revealed that the radial basis function neural network and the time-delay recurrent neural network exhibited consistent results, compared to BPN and the recurrent neural network. The results also pointed out that different percentages of training data were suitable for predicting consumer ETFs' performance based on high and low grey relationship grade variables. Evidence has shown that the ETFs in Brazil and China are more predictable than those in other countries. All ANN models' results indicated that the use of 10% testing data could predict consumer ETFs better, particularly the ETFs of the United States (US) and those excluding the United States (EX-US). The Diebold–Mariano (DM) test results suggest that the best predictability model for consumer ETFs is BPN, which is significantly superior to other models.