| dc.description.abstract | This thesis predicts one-day ahead adjusted closing price estimates of four different
stocks listed at the Oslo Stock Exchange. The prediction uses a rolling window
approach, utilizing 60 days of historical adjusted closing prices as input for each
observation. The thesis conducts a comparative analysis, evaluating the prediction
performance of five different models: Ordinary Least Squares, Support Vector
Regression, Random Forest, Extreme Gradient Boosting, and Long Short-Term
Memory networks.
The findings of the comparative analysis indicate that the Long Short-Term Memory
(LSTM) network consistently outperforms the other models for all the stocks
considered. These findings align with previous research that highlights the
effectiveness of LSTM models in stock market prediction. While the thesis does not
provide a definitive conclusion on market efficiency or inefficiency, the predictive
performance of the LSTM model suggests the presence of potential inefficiencies in
the market. Additionally, this thesis identifies key hyperparameters used to avoid
overfitting as well as optimizing predictive performance for the LSTM model.
Overall, this research contributes to the existing literature by investigating the
effectiveness of the models on a different market, the Norwegian stock market, as
well as noting the importance of model specific hyperparameter tuning. | en_US |
