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The forecasting of consumer exchange-traded funds (ETFs) via grey relational analysis (GRA) and artificial neural network (ANN) by Maya Malinda; Jo-Hui Chen is a Economics, Econometrics and Finance article available to read on EtoBox.

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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

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Author

Maya Malinda; Jo-Hui Chen

Publisher

Springer Science and Business Media LLC

Published

2021

Language

EN

Field

Economics, Econometrics and Finance (Social Sciences)