The Bombay Stock Exchange (BSE) is India’s premier and most prestigious stock market. A stock market is a facilitation centre for trading (buying and selling) of stocks of various companies. Its index is calculated as a combination of stock prices of several companies enlisted in the exchange. The stock market is characterized by its endless and unpredictable troughs and crests. This paper attempts to analyse the BSE Sensex Closing Index data over a span of the last decade collected on the last day of the month (from May, 2009 to April, 2019). It also attempts to predict the BSE Sensex closing data for a future span of 10 years at a monthly frequency. The paper also does a accuracy testing of the predictive model generated. This work would be beneficial for both the nation and a trading individual. The Stock market indices reflect the health of a nation’s economy and its direction and growth. A trading individual would benefit in his pursuit of profit making by taking correct investment decisions based on accurate predictions made. The paper uses the ARIMA model for timeseries analysis and for generating a predictive model for making future forecasts.

Arima model, Sensex forecasting, Short-term prediction, Stock market prediction, Time Series analysis

[1] D. Banerjee, "Forecasting of Indian stock market using time-series ARIMA model", In the proceedings of 2nd IEEE International Conference on Business and Information Management (ICBIM), pp. 131-135 , January 2014.
[2] Ayodele A. Adebiyi, Aderemi O. Adewumi, Charles K. Ayo , "Stock price prediction using the ARIMA model" , In proceedings of the 16th IEEE International Conference on Computer Modelling and Simulation (UKSim) , pp. 106 -112 , March 2014 .
[3] A. Edward, J. Manoj, “Forecast model using arima for stock prices of automobile sector” , International Journal of Research in Finance and Marketing , pp. 1-9 , April 2016.
[4] Mohamed Ashik. A, Senthamarai Kannan. K , “Forecasting national stock price using arima model”, Global and Stochastic Analysis , pp. 71-81, January 2017.
[5] M. Angadi, A. Kulkarni , “Time Series Data Analysis for Stock Market Prediction using Data Mining Techniques with R”, International Journal of Advanced Research in Computer Science , Pp. 104-108, August 2015.
[6] P. Mondal, L.Shit , S.Goswami , “Study of effectiveness of time series modeling (ARIMA) in forecasting stock prices”, International Journal of Computer Science, Engineering and Applications, pp. 13-29, April 2014.
[7] Kamalakannan J , I. Sengupta , S. Chaudhury , “Stock Market Prediction using TimeSeriesAnalysis” , Computing Communications and Data Engineering Series , Volume No. 01, Issue No. 03 , pp. 1-5 , 2018 .
[8] J.V.N. Lakshmi , Ananthi Sheshasaayee, “A Big Data Analytical Approach for Analyzing Temperature Dataset using Machine Learning Techniques” , International Journal of Scientific Research in Computer Sciences and Engineering , Volume No. 05 ,Issue No. 03, pp. 92-97 , June 2017.
[9] Himanshi , Komal Kumar Bhatia, “ Prediction Model for Under-Graduating Student’s Salary Using Data Mining Techniques” , International Journal of Scientific Research in Network Security and Communication, Volume No. 06 ,Issue No. 02, pp. 50-53 , April 2018.