This study compares the price predictions of the Vanguard real
estate exchange-traded fund (ETF) (VNQ) using the back propagation neural
network (BPNN) and autoregressive integrated moving average (ARIMA) models. The
input variables for BPNN include the past 3-day closing prices, daily trading
volume, MA5, MA20, the S&P 500 index, the United States (US) dollar index,
volatility index, 5-year treasury yields, and 10-year treasury yields. In
addition, variable reduction is based on multiple linear regression (MLR). Mean
square error (MSE), mean absolute error (MAE), and mean absolute percentage
error (MAPE) are used to measure the prediction error between the actual
closing price and the modelsí forecasted price. The training set covers the
period between January 1, 2015 and March 31, 2020, and the forecasting set
covers the period from April 1, 2020 to June 30, 2020. The empirical results
reveal that the BPNN modelís predictive ability is superior to the ARIMA
modelís. The predictive accuracy of BPNN with one hidden layer is better than
with two hidden layers. Our findings provide crucial market factors as input
variables for BPNN that might inspire investors in VNQ markets.
JEL classification numbers: C32, C45, C53, G17.
Vanguard real estate ETF (VNQ), Back propagation
neural network (BPNN), Autoregressive integrated moving average (ARIMA),
Multiple linear regression (MLR).