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

• Published in: Abhigyan (New Delhi) Vol. 37; no. 4; pp. 12 - 20
• Main Author: Pradhan, Abhilas Kumar
• Format: Journal Article
• Language: English
• Published: New Delhi, India SAGE Publications 01.01.2020; Foundation for Organisational Research & Education; Foundation for Organizational Research and Education (FORE)
• Subjects: Equity; Finance; Forecasting techniques; Gold; Prediction models; Prices; Refineries; Stock prices; Variants; India
• DOI: 10.56401/Abhigyan_37.4.2020.12-20
• ISSN: 0970-2385

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 literature 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. To 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.