Prediction of Banking Sectors Financial Data of Dhaka Stock Exchange Using Autoregressive Integrated Moving Average (ARIMA) Approach

Abstract

This paper investigates the predictability with the banking sector data of the Dhaka Stock Exchange (DSE) by using the Autoregressive Integrated Moving Average (ARIMA) process. Through different formal tests on the data set, the best-fitted model selected was ARIMA (0,2,1) for the data series. This study was select five banks from DSE such as Al-Arafah bank limited, EXIM bank limited, Islami bank limited, National bank limited, and one bank limited and use these data to train the model and checks the predictive power of the model. Only analyzed results of Al-Arafah bank limited are presented in this paper because the same results have been produced for other remaining companies. The obtained results show that all the companies closing stock prices are non-stationary. It is also found that the original value curve and the predicted value curve are very much identical. So, the fitted model is performed better. For the validity of the model, the root means squared error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) were checked.