Predicting stock return and volatility with machine learning and econometric models— a comparative case study of the Baltic stock market

dc.contributor.advisorSharma, Rajesh, juhendaja
dc.contributor.advisorEratalay, Mustafa Hakan, juhendaja
dc.contributor.advisorLapitskaya, Darya, juhendaja
dc.contributor.authorNõu, Anders
dc.contributor.otherTartu Ülikool. Loodus- ja täppisteaduste valdkondet
dc.contributor.otherTartu Ülikool. Arvutiteaduse instituutet
dc.date.accessioned2023-09-13T08:44:28Z
dc.date.available2023-09-13T08:44:28Z
dc.date.issued2021
dc.description.abstractPredicting 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.urihttps://hdl.handle.net/10062/92129
dc.language.isoenget
dc.publisherTartu Ülikoolet
dc.rightsopenAccesset
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectmachine learninget
dc.subjectneural networkset
dc.subjectautoregressive moving averageet
dc.subjectgeneralized autoregressive conditional heteroskedasticityet
dc.subject.otherbakalaureusetöödet
dc.subject.otherinformaatikaet
dc.subject.otherinfotehnoloogiaet
dc.subject.otherinformaticset
dc.subject.otherinfotechnologyet
dc.titlePredicting stock return and volatility with machine learning and econometric models— a comparative case study of the Baltic stock marketet
dc.typeThesiset

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