Machine Learning and Time Series Models for VNQ Market Predictions

Abstract

Abstract 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 models’ 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 model’s predictive ability is superior to the ARIMA model’s. 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. Keywords: Vanguard real estate ETF (VNQ), Back propagation neural network (BPNN), Autoregressive integrated moving average (ARIMA), Multiple linear regression (MLR).