Publication Type : Conference Paper
Publisher : 2017 IEEE International Conference on Computational Intelligence and Computing Research, ICCIC 2017
Source : 2017 IEEE International Conference on Computational Intelligence and Computing Research, ICCIC 2017, Institute of Electrical and Electronics Engineers Inc. (2018)
Url : https://www.scopus.com/inward/record.uri?eid=2-s2.0-85057999926&doi=10.1109%2fICCIC.2017.8524232&partnerID=40&md5=5be03e4f4737b36da0fd6072c276b9ce
ISBN : 9781509066209
Keywords : Abnormal returns, ARIMA modeling, Artificial intelligence, Automatic ARIMA, Commerce, Dynamic forecasting, Electronic trading, Financial markets, Forecasting, Investments, Mean absolute percentage error, Multivariate time series, Predictive models, Stock exchange
Campus : Coimbatore
School : School of Business
Year : 2018
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. © 2017 IEEE.
Cite this Research Publication : L. Yermal and Dr. P. Balasubramanian, “Application of Auto ARIMA Model for Forecasting Returns on Minute Wise Amalgamated Data in NSE”, in 2017 IEEE International Conference on Computational Intelligence and Computing Research, ICCIC 2017, 2018.