Forecasting Stock Market Series with ARIMA Model

Fatai Adewole Adebayo, Ramysamy Sivasamy and Dahud Kehinde Shangodoyin

Journal of Statistical and Econometric Methods, 2014, vol. 3, issue 3, 3

Abstract: Forecasting financial time series such as stock market has drawn considerable attention among applied researchers because of the vital role which stock market play on the economy of any nation. To date, autoregressive integrated moving average (ARIMA) model has remained the mostly widely used time series model for forecasting stock market series the problem of selecting the best ARIMA model for stock market prediction has attracted a huge literature in empirical analysis in view of its implication for national economics. In this paper, we consider the problem of selecting best ARIMA models for stock market prediction for Botswana and Nigeria. Using the standard model selection criteria such as AIC, BIC, HQC, RMSE and MAE we evaluate the forecasting performance of various candidate ARIMA models with a view to determining the best ARIMA model for predicting stock market in each country under investigation. The outcome of the empirical analysis indicated that ARIMA (3,1,1) and ARIMA (1,1,4) models are the best forecast models for Botswana and Nigeria stock market respectively.

Date: 2014
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