Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/146095
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dc.contributor.authorLim, Sze Chien_US
dc.date.accessioned2021-01-26T07:41:07Z-
dc.date.available2021-01-26T07:41:07Z-
dc.date.issued2017-
dc.identifier.urihttps://hdl.handle.net/10356/146095-
dc.description.abstractFinancial markets facilitate international trade, are indicative of the future prospects of organizations and economies, and are drivers of economic growth (Hsu, Lessmann, Sung & Johnson, 2016). Hence, the prediction of financial market assets with reference to previously observed data has drawn considerable attention as an active research area (Zhu, Wang, Xu & Li, 2008). The financial market is a non-linear dynamic system that is influenced by many interdependent factors (Abu-Mostafa & Atiya, 1996). Such are macroeconomics, political sentiments, news, general economic conditions as well as the expectations and psychology of active investors (Novak & Veluscek, 2015). As a result of these ambiguous complexities coupled with the large amount of noise in financial market data, modelling stock trends has been regarded as a challenging task (Polimenis & Neokosmidis, 2014). This paper therefore addresses the stock trend prediction problem as a classification task and models it using Support Vector Machine (SVM). It also explores different feature selection algorithms applicable for SVM and finally draw comparisons amongst results generated by other machine learning methods.en_US
dc.language.isoenen_US
dc.publisherNanyang Technological Universityen_US
dc.subjectScience::Mathematicsen_US
dc.subjectEngineering::Computer science and engineering::Computing methodologies::Artificial intelligenceen_US
dc.titleForecasting stock trend direction with support vector machineen_US
dc.typeFinal Year Project (FYP)en_US
dc.contributor.supervisorPUN Chi Sengen_US
dc.contributor.schoolSchool of Physical and Mathematical Sciencesen_US
dc.description.degreeBachelor of Science in Mathematical Sciencesen_US
dc.contributor.supervisoremailcspun@ntu.edu.sgen_US
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Appears in Collections:SPMS Student Reports (FYP/IA/PA/PI)
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