Predicting stock return and volatility with machine learning and econometric models— a comparative case study of the Baltic stock market
| dc.contributor.advisor | Sharma, Rajesh, juhendaja | |
| dc.contributor.advisor | Eratalay, Mustafa Hakan, juhendaja | |
| dc.contributor.advisor | Lapitskaya, Darya, juhendaja | |
| dc.contributor.author | Nõu, Anders | |
| dc.contributor.other | Tartu Ülikool. Loodus- ja täppisteaduste valdkond | et |
| dc.contributor.other | Tartu Ülikool. Arvutiteaduse instituut | et |
| dc.date.accessioned | 2023-09-13T08:44:28Z | |
| dc.date.available | 2023-09-13T08:44:28Z | |
| dc.date.issued | 2021 | |
| dc.description.abstract | Predicting the stock market is a widely researched area of study that is a challenging task. The nature of the problem lies in correctly forecasting the direction and the magnitude of the stock market movement. The severity of the problem exists due to the stock market being impacted by a multitude of factors. There are numerous ways to analyse the stock market and make appropriate investment decisions, but it is challenging to decide the best approach. Here we show which approach is more effective in predicting the returns and volatility of the Baltic stock market: the machine learning or econometric approach. There is a low amount of research on using machine learning or econometric models to predict the Baltic stock market. However, there are no comparative researches that offer a fair comparison between the different approaches for the Baltic stock market. Regarding results, the lowest symmetric mean absolute percentage error for the support vector regression model is 61.90%, and for the autoregressive moving average model, it is 165.43%. The lowest symmetric mean absolute percentage error for GARCH is 51.05%, and for the GARCH-ANN model, it is 61.65%. Overall, the machine learning models outperform the econometric models in most of the evaluated metrics. However, the econometric models’ results are comparable to the machine learning models’ results in most cases. | et |
| dc.identifier.uri | https://hdl.handle.net/10062/92129 | |
| dc.language.iso | eng | et |
| dc.publisher | Tartu Ülikool | et |
| dc.rights | openAccess | et |
| dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 International | * |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
| dc.subject | machine learning | et |
| dc.subject | neural networks | et |
| dc.subject | autoregressive moving average | et |
| dc.subject | generalized autoregressive conditional heteroskedasticity | et |
| dc.subject.other | bakalaureusetööd | et |
| dc.subject.other | informaatika | et |
| dc.subject.other | infotehnoloogia | et |
| dc.subject.other | informatics | et |
| dc.subject.other | infotechnology | et |
| dc.title | Predicting stock return and volatility with machine learning and econometric models— a comparative case study of the Baltic stock market | et |
| dc.type | Thesis | et |