Application of Auto ARIMA Model for Forecasting Returns on Minute Wise Amalgamated Data in NSE

Traders dealing in stock markets often apply various means to generate models that can forecast prices of stocks aimed at making abnormal returns on their investment. Such predictive models often takes into account the past price movements and the volatility of the stocks. Earlier studies have used both univariate and multivariate time series methods to generate forecasts. This paper explores the usage of Automatic ARIMA function using Eviews 9.5 for forecasting the stock returns on minute by minute data for 50 stocks in the National Stock Exchange in India. The study finds that Auto ARIMA applied on the sample generates satisfactory forecasts identified by low Mean Absolute Percentage Error (MAPE) only for 3 companies while the forecasts for the others were found to be weak.