Predicting Long Term Prices of Nifty Index using Linear Regression and ARIMA: A comparative study

Abstract

Stocks are traded continuously in the financial market, creating huge time series data. Stock market time series is very volatile and highly complex to model. There are many methods to forecast a time series. This study predicts and compares the performance of two statical methods, linear regression, and ARIMA also referred to as the Box Jenkins method, which stands for Autoregressive Integrated Moving Average, is a robust time series forecasting technique used frequently by researchers. Simple linear regression is generally assumed unsuitable for non-linear stock time series data. However, a literature gap exists in comparing linear regression and the ARIMA method for forecasting stock prices. Hence, this study compares the ARIMA and linear regression methods to forecast stock prices using daily and weekly NIFTY data. Further, this study also experiments using a different length of time series, namely 1-year and 2-year data. The result shows that ARIMA outperformed regression on daily and weekly data when the test for 1 year of data. When 2-year data is taken, linear regression outperformed ARIMA on both daily and weekly data. Hence, the linear regression model and ARIMA are sensitive to input parameters such as the number of days for training and forecasting. An automated method or algorithm could improve the robustness of the model. Further, as stock price data is non-linear, machine learning algorithms such as neural networks and support vector regression can be more suitable for prediction.