ARIMA Time Series Analysis in Forecasting Daily Stock Price of Chittagong Stock Exchange (CSE)

Abstract

The aim of the study is to examine the nature of daily share price and select a suitable ARIMA model to forecast the future daily share price from the previous daily share price of Chittagong Stock exchange (CSE). A random sampling method has been followed to collect the closing price of 60 companies for the period of January 2019 to December 2019 (241 trading days). Durbin-Watson test has been conducted to find the autocorrelation in each of the share prices. Then the Augmented Dickey-Fuller test has been applied to test the stationary of data and the Autocorrelation function (ACF) and Partial Autocorrelation function (PACF) has been calculated to determine the lag value of moving average MA(q) and autocorrelation AR(p)based on Ljung-Box Test Q, root mean square error, mean absolute error, mean absolute percent error and R-square values. After selecting ARIMA (p,d,q) model, forecasted values for each of the shares are calculated for the next 22 trading days of January 2020. Then a comparison has been made between the forecasted prices and the actual share prices by using the Goodness-of-fit Test, Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), Mean Square Error (MSE) to validate the model. The result shows that the ARIMA model is applicable to forecast the daily share price of CSE.