Forecasting the OMXS30 - a comparison between ARIMA and LSTM

• Main Authors: Andréasson, David, Mortensen Blomquist, Jesper
• Format: Others
• Language: English
• Published: Uppsala universitet, Statistiska institutionen 2020
• Subjects: machine learning; deep learning; neural networks; RNN; time series; stock market; index; Probability Theory and Statistics; Sannolikhetsteori och statistik
• Online Access: http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-413793

Machine learning is a rapidly growing field with more and more applications being proposed every year, including but not limited to the financial sector. In this thesis, historical adjusted closing prices from the OMXS30 index are used to forecast the corresponding future values using two different approaches; one using an ARIMA model and the other using an LSTM neural network. The forecasts are made on three different time intervals: 90, 30 and 7 days ahead. The results showed that the LSTM model performs slightly better when forecasting 90 and 30 days ahead, whereas the ARIMA model has comparable accuracy on the seven day forecast.