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Original Research Article STOCK price prediction is an important topic in financial statistics which stimulates the interest over the years to develop better predictive models. It also provides a way to predict and perhaps avoid the risk of large adverse changes in price. Autoregressive Integrated Moving Average (ARIMA) methodology: “A real time risk prediction technique, flexible in computing and universal approximate because of its simplicity and wide acceptability that can be applied to a wide range of forecasting problems with a high degree of accuracy for the convenience of predicting the future value in share market and gives a better future scope for investment” is used in this paper to forecast the S&P BSE stock price. Before forecasting the stock prices using ARIMA model, a trend analysis was conducted on the sample data to find out the nature of the time series, i.e. upward trend, stationary or downward trend. The skewness and kurtosis also give the basic idea about the shape of the time series data. Augmented Dickey – Fuller (ADF) and Phillips – Perron (PP) unit root test were applied to know about the stationarity of the time series data. A robust model was identified by comparing the smallest value of Akaike‟s Information Criteria (AIC) and Bayesian Information Criteria (BIC). The parameters: R-Square, Adjusted R-Square, S.E of regression and Durbin-Watson statistic were estimated to regulate best ARIMA model. After parameter estimation is done, it is necessary to verify the satisfactoriness of the estimated model. The value of serial correlation was studied to verify that the series of correlation residuals is white noise or not. After the speculative model has been fitted, Ljung-Box Q statistic was applied for diagnostic checking or suitability of the model. Forecasting is the next step of the ARIMA model, which is an essential part of time series analysis. It is the predicted values based on identified past values of that variable or other related variable. Mean Absolute Percentage Error (MAPE) was calculated to determine the forecast accuracy as well as performance of the model.

scholars journal of physics, mathematics and statistics

Application of Box – Jenkins ARIMA (p, d, q) Model for Stock Price Forecasting and Detect Trend of S&amp;P BSE Stock Index: An Evidence from Bombay Stock Exchange