Comparison of ARIMA and Exponential Smoothing Models in Prediction of Stock Prices

Abstract

Stock prices tend to show trends or seasonality or have random walk movements. Time series statistical models developed over time aid prediction of stock prices to assist informed decision-making for investors. These models provide quantitative information to financial specialists at the time of placing their buy–sell orders. The paper compares the movement of two univariate time series using two forecasting models—exponential smoothing and autoregressive integrated moving average (ARIMA) (p; d; q). We predict stock prices of selected 15 companies across three sectors (banking, pharmaceuticals, and Information technology) from NIFTY 50 data for the period April 01, 2016 to March 31, 2021. All these 15 companies are representative constituents of the three sectors within the Nifty 50 index. Performances of models were assessed through forecasting error measures such as root mean square error and mean absolute percentage error. Performances of both models were identical for nine stocks. Prediction based on ARIMA was more accurate for six stocks, whereas exponential smoothing model was a better indicator of stock prices in the case of one stock. However, the differences in error measures of the both the models are marginal, and parsimony principle may drive the choice of model.