Abstract
Stock price forecasting is, and will always be, one of the most imperative financial
conjectures investors are confronted with. There are plentiful ways of effectively
forecasting a company’s share price, most of which rely on various factors that have a
bearing on the market price of shares. This paper, however, has employed a method of
forecasting which is based on the previous values of the variable itself. This method,
technically known as the ARIMA methodology, was developed by Box and Jenkins in
1970. The current paper employed this method on stock prices of one of the largest
companies in Pakistan, i.e. Oil & Gas Development Company Limited (OGDCL). Daily
adjusted closing stock prices of the company were taken from 2004 to 2018 covering
almost 15 years with 3632 observations. Results showed that some of the ARIMA
archetypes used in the study had a strong potential for prediction in the short run. It was,
therefore, deduced that ARIMA modeling works pretty efficiently for short-term
prediction. Investors in stocks may use the findings of the study to supplement their
forecasting aptitude.
Mustafa Afeef, Anjum Ihsan, Hassan Zada. (2018) Forecasting Stock Prices through Univariate ARIMA Modeling, NUML International Journal of Business & Management, Volume 13, Issue 2.
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