ARIMA Modeling : Forecasting Indices on the Stockholm Stock Exchange

Sammanfattning: The predictability of the stock market has been discussed over a long period of time and is of great interest to anyone investing in the stock market. Some people argue that the stock market is impossible to predict, while others believe that the market is somewhat predictable. The purpose of this study is to construct, evaluate, and compare different ARIMA models’ ability to forecast the Stockholm Stock Exchange indices, OMXSPI and OMXS30. To find the best‑fitted model for each index respectively, the Expert Modeler tool in the software SPSS is used. In the analysis, the suggested model is confirmed by following the Box-Jenkins methodology. Furthermore, Akaike’s information criterion is used to show that this model has the best fit, compared to another estimated model. Statistical measurements that aim to measure the performance of time series forecasting models are used to compare the best‑fitted model for each index to a model with worse fit, based on the data. To enable comparison between the models, out-of-sample predictions are made for already occurred periods and the measurements used measures the mean percentage error from the actual outcomes. Thereafter, the performances of the best‑fitted models are compared to the performances of models that are used in a previous study, where time series have been predicted using ARIMA models.   The results of the study showed that the models suggested by SPSS performed better in forecasting the indices compared to the models with worse fit, based on the data. Compared to the models used in a previous study, the out-of-sample forecast performances of the models in this study are in line with those of that previous study. The mean percentage error for the forecasted values of OMXSPI and OMXS30 were 4,74% and 2,22%, respectively. The mean absolute percentage error for the forecasted values were 6% for OMXSPI and 4,35% for OMXS30.