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Forecasting Daily Equity Price Using Auto Regressive Integrated Moving Average (ARIMA) Model: An Application to Shirpur Gold Refinery Ltd., India


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1 Indira School of Business Studies, Pune, India
     

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Forecasting is a focal subject in the area of finance and economics which has urged the interest of researchers and financial analysts to develop better predictive models. The Autoregressive Integrated Moving Average (ARIMA) models have been explored in the liter ature and are extensively used in prediction of variables with temporal force. This paper has endeavored equity price prediction using ARIMA procedure expending 246 daily closing prices. T o this end, daily equity prices for Shirpur Gold Refinery Ltd., India from April 2017 to March 2018 has been considered to build an appropriate ARIMA model employing R software. The best obtained model, ARIMA (0, 1, 1) of its several variants has been used for securing equity price prediction intervals for the next few days. Additional effort was made to judge predictive performance of the fitted model taking out-of-sample closing equity prices. The results divulged that ARIMA model has a strong potential for short-term prediction and can contest favorably with other forecasting techniques used in stock price prediction.

Keywords

ARIMA Model, Equity Price, Differencing, R-software, Confidence Interval, Autoregressive, Moving Average.
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  • Adebiyi, A. A., Adewumi, A. O., & Ayo, C. K. (2014). Stock Price Prediction Using the ARIMA Model. 2014 UKSim-AMSS 16th International Conference on Computer Modelling and Simulation. 105-111,doi:DOI 10.1109/UKSim.2014.67.

  • Chujai, P ., Kerdprasop, N., & Kerdprasop, K. (2013). Time Series Analysis of Household Electric Consumption with ARIMA and ARMA Models. Proceedings of the International Multi Conference of Engineers and Computer Scientists, 1. Hong Kong.

  • Conejo, A. J., Plazas, M. A., Espinola, R., & Ana, M. B. (2005). Day-ahead electricity price forecasting using the wavelet transformation and ARIMA models. IEEE T ransactions Power System, 20(2), 1035-1042.

  • Dritsaki, C. (2015). Forecasting real GDP rate through econometric models: An empirical study from Greece. J ournal of International Business and Economics, 3(1), 13-19.

  • Farooqi, A. (2014). ARIMA model building and forecasting on imports and exports of Pakistan. Pakistan J ournal of Statistics and Operation Research, 10(2), 157-168.

  • Jadav , B.V ., Reddy , B.V .C., & Gaddi, G.M. (2017). Application of ARIMA model for forecasting agricultural prices. J ournal of Agricultural Science and T echnolog y , 19, 981-992.

  • Jadav , A.V ., & Kamble K.B. ( 2017). Prediction of stock prices in oil sectors using ARIMA model. International Journal of Mathematics T rends and T echnolog y , 51(4), 266-270.

  • Javier , C., Rosario, E., Francisco, N. J., & Conejo, A. J. (2003). ARIMA models to predict next-day electricity prices. IEEE T ransaction. Power Systems,18(3),1014-1020.

  • Kasyoki., & Alexander (2015). Simple steps for fiting Arima model to time series data for forecasting using R. International Journal of Science and Research, 4(3), 318-321.

  • Littleman, R. B. (1986). Forecasting with Bayesian V ector Autoregressions-five years of experience. Journal of Business and Economic Statistics, 4(1), 25-38.

  • Maity , B., & Chatterjee, B. (2012). Forecasting GDP growth rates of India: An emperical study . International Journal of Economics and Management Science,1(9), 52-58.

  • Merh, N., Saxena, V . P ., & Pardasani, K. R. (2010). A comparison between hybrid approaches of ANN and ARIMA for Indian stock trend forecasting. J ournal of Business Intelligence, 3(2), 23-43.

  • Merh, N., Saxena, V . P ., & Pardasani, K. R. (2011). Next day stock market forecasting: An application of ANN & ARIMA. The IUP Journal of Applied Science,17(1), 70-84.

  • Mondal, P ., Shit, L., & Goswami, S. (2014). Study of effectiveness of time series modeling (ARIMA) in forecasting stock prices. International J ournal of Computer Science, Engineering and Applications, 4(2), 13-29.

  • Ning, W ., Kuan-jiang, B., & Zhi-fa, Y . (2010). Analysis and forecast of Shaanxi GDP based on the ARIMA model. Asian Agricultural Research, 2(1), 34-41.

  • Paul, J. C., Hoque, M., & Rahman, M. M. (2013). Selection of best ARIMA model for forecasting average daily share price index of pharmaceutical companies in Bangladesh: A case study on square pharmaceutical ltd. Global J ournal of Management and Business Research Finance, 13(3), 14-26.

  • Samer , S., Elie, B., & Nasr , G. (2001). Univariate modeling and forecasting of energy consumption: The case of electricity in Lebanon. Energy , 26(1), 1-14.

  • Stockton, D. J., & Glassman, J. E. (1987). An evolution of the forecast performance of alternative models of inflation. The Review of Economics and Statistics, 69, 108-117.

  • Uma Devi, B., Sundar , D., & Ali, P . (2013). An effective time series analysis for stock trend prediction using ARIMA model for Nifty Midcap-50. International J ournal of Data Mining and Knowledge Management Process, 3(1), 65-78.

  • Wabomba, M. S., Mutwiri, M. P ., & Fredrick, M. (2016). Modeling and forecasting Kenyan GDP using autoregressive integrated moving average (arima) models. Science J ournal of Applied Mathematics and Statistics, 4(2), 64-73.

  • Wadi, S.AL., Almasarweh, M., & Alsaraireh, A. A. (2018). Predicting closed price time series data using ARIMA model. Modern Applied Science, 12 (11), 181-185.

  • www .bseindia.com. Retrieved from https://www .bseindia.com / markets / equity/EQReports / Stock PrcHistori. aspx?scripcode = 512289&flag=sp&Submit=G.

  • Zakai, M. (2014). A time series modeling on GDP of Pakistan. J ournal of Contemporary Issues in Business Research, 3(4), 200-210.

  • Zhang, H. (2013). Modeling and forecasting regional GDP in Sweden using autoregressive models. Working paper . Sweden: Högskolan Dalarna University .


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  • Forecasting Daily Equity Price Using Auto Regressive Integrated Moving Average (ARIMA) Model: An Application to Shirpur Gold Refinery Ltd., India

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Authors

Abhilas Kumar Pradhan
Indira School of Business Studies, Pune, India

Abstract


Forecasting is a focal subject in the area of finance and economics which has urged the interest of researchers and financial analysts to develop better predictive models. The Autoregressive Integrated Moving Average (ARIMA) models have been explored in the liter ature and are extensively used in prediction of variables with temporal force. This paper has endeavored equity price prediction using ARIMA procedure expending 246 daily closing prices. T o this end, daily equity prices for Shirpur Gold Refinery Ltd., India from April 2017 to March 2018 has been considered to build an appropriate ARIMA model employing R software. The best obtained model, ARIMA (0, 1, 1) of its several variants has been used for securing equity price prediction intervals for the next few days. Additional effort was made to judge predictive performance of the fitted model taking out-of-sample closing equity prices. The results divulged that ARIMA model has a strong potential for short-term prediction and can contest favorably with other forecasting techniques used in stock price prediction.

Keywords


ARIMA Model, Equity Price, Differencing, R-software, Confidence Interval, Autoregressive, Moving Average.

References