International Research Journal of Commerce , Arts and Science

 ( Online- ISSN 2319 - 9202 )     New DOI : 10.32804/CASIRJ

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STUDY ON RETURN PREDICTION OF THE CONSTRUCTED PORTFOLIO USING ARIMA MODELING

    1 Author(s):  ABIN BABY

Vol -  11, Issue- 1 ,         Page(s) : 11 - 22  (2020 ) DOI : https://doi.org/10.32804/CASIRJ

Abstract

Stock return prediction is an important topic in finance and economics which has encouraged the attention of researchers over the years to develop improved predictive models. The autoregressive integrated moving average (ARIMA) models have widely used for time series prediction. This paper used an extensive process of building stock return predictive model using the ARIMA model. Published stock data obtained National stock exchange of Indian(NSE) are used with stock price predictive model developed. Results obtained revealed that the ARIMA model has a strong potential for short-term prediction and can compete favorably with existing techniques for stock price prediction.

Ayodele, A. A., Adewumi, A. O., & Ayo, C. K. (2014). Stock Price Prediction Using the ARIMA Model. UKSim-AMSS, 105-111.
Banumathy, K., & Azhagaiah, R. (2013). Modeling Stock Market Volatility: Evidence from India. Managing Global Transitions, 13(1), 27-42.
Jadhav, S., Kakade, S., Utpat, K., & Deshpande, H. (2015, November). Indian Share Market Forecasting with ARIMA Model. International Journal of Advanced Research in Computer and Communication Engineering, 4(11), 334-336.
Karmakar, M. (2005, September). Modeling Conditional Volatility of the Indian Stock Markets. vikalpa, 30(3), 21-37.
Mondal, P., Shit, L., & Goswami, S. (2014, April). Study of effectiveness of time series modeling (ARIMA) in forecasting stock prices. International Journal of Computer Science, Engineering and Applications (IJCSEA), 4(2), 13-29.
Mutendadzamera, S., & Mutasa, F. K. (2014). Forecasting stock prices on the Zimbabwe Stock Exchange (ZSE) using Arima and Arch/Garch models. International Journal of Management Sciences, 3(6), 419-432.
Ou, P., & Wang, H. (2011, July). Modeling and Forecasting Stock Market Volatility by Gaussian Processes based on GARCH, EGARCH and GJR Models. Proceedings of the World Congress on Engineering, 1(1), 1-5.
U, J., & Suresh, K. (2014, February). Estimating Stock Market Volatility Using Non-linear Models. IOSR Journal of Business and Management (IOSR-JBM), 16(2), 62 -65.

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