Modelling and Forecasting Daily Stock Price of NMB Bank in Tanzania

The stock market is an organized place of trading of securities by means of electronic exchanges. It is one of the most vital areas of a market economy because it provides companies with access to capital by facilitating investors to buy or sell shares with the sole objective of making a profit. Stock prices can be treated as a discrete-time series model based on a set of well-defined numerical data items collected at successive points at regular intervals of time. Since it is essential to identify a model to analyze trends of stock prices with adequate information for decision making, it is recommended that transforming the time series data by using various statistical models and then after picking the best-fitted model is a better algorithmic approach than forecasting directly, as it gives more authentic and reliable results. In this article, we have made a comparison on a short-term basis of the autoregressive integrated moving average (ARIMA) model and Simple Exponential Smoothing (SES), Double exponential smoothing (DES) and Damped Trend Linear Exponential Smoothing (DTLES) techniques to the daily stock price data of NMB bank in Tanzania. The modeling process was preceded by analyzing the time series of interest, which revealed non-stationarity. Among the fitted models, the best model was selected by using Akaike’s information criterion (AIC) and Bayesian Information Criterion (BIC). The DTLES was selected as the best model for NMB bank daily stock prices in Tanzania. It was also revealed that the best time series model fits well in the historical data and may be used for short-term forecasting of the future values of NMB bank stock prices in Tanzania.