Stock Market prediction is an important topic in finance and economics which has encouraged the interest of researchers over the years to develop better predictive models. The autoregressive integrated moving average (ARIMA) models have been explored in literature for time series prediction. This paper presents extensive process of building stock indices predictive model using the ARIMA model. Published stock indices data obtained from National Stock Exchange (NSE) are used with stock indices price predictive model developed. At first the stationarity condition of the data series are observed by ACF and PACF plots, then checked using the statistics such as Ljung-Box-Pierce Q-statistic and Dickey-Fuller test statistic. Results obtained revealed that the ARIMA (1,1,2) model has a strong potential for short-term prediction and Adjusted ARIMA gives more accurate forecasting compare to ARIMA.

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