Comparison of Trend Forecast Using ARIMA and ETS Models for S&P500 Close Price

Abstract

Stock price forecast is pivotal for various financial and economic institutions and individuals. The aim of this study is to present viable and general approaches that would improve the understanding of forecasting stock market close price of individual stock. This paper explains processes of applying methods including Autoregressive Integrated Moving Average (ARIMA) and Exponential Smoothing (ETS) on the close price data of S&P500 index, but the ticker of the stock can be swapped for forecasting other stocks. In terms of determining the accuracy of the models, we center on the simplest methodology. Of the two models involved in this study, we compare them on the basis of standard deviation. Stock data are obtained from yahoo finance using quantmod package in R studio. Forecasting result shows that the ARIMA model has a better fit with the data and can give a promising general trend prediction compared with existing methods.