On modelling OMXS30 stocks - comparison between ARMA models and neural networks

Abstract: This thesis compares the results of the performance of the statistical Autoregressive integrated moving average (ARIMA) model and the neural network Long short-term model (LSTM) on a data set, which represents a market index. Both models are used to predict monthly, daily, and minute close prices of the OMX Stockholm 30 Index. Chosen data were preprocessed, models were fitted to data and their prediction was evaluated and compared. To evaluate forecast accuracy as well as to compare two models fitted to a financial time series, we have used the two performance measures: mean square error (MSE) and mean absolute percentage error (MAPE). In addition, the computation time of fitting models was measured in this thesis to evaluate and compare the computational workload associated with the two models. Also, other factors were discussed, such as the number of parameters and explainability. The analysis revealed that the minute and the daily data of the OMX 30 Stockholm index closely resembled white noise, indicating random fluctuations. However, for the monthly data, the LSTM model outperformed the ARIMA model in terms of MSE, with values of 15,230 and 14,380, respectively. Additionally, the LSTM model demonstrated superior capability in capturing the dynamics of price movement compared to ARIMA. Regarding MAPE, both models exhibited similar values, with ARIMA at 4.8 and LSTM at 4.9. In addition, the ARIMA model had significantly fewer parameters compared to the LSTM model and offered the advantages of being more transparent and easier to interpret.