Automated stock trading : a multi-agent, evolutionary approach

 

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dc.contributor.advisor Potgieter, Anet en_ZA
dc.contributor.author Kruger, Kurt en_ZA
dc.date.accessioned 2015-11-08T05:15:11Z
dc.date.available 2015-11-08T05:15:11Z
dc.date.issued 2008 en_ZA
dc.identifier.citation Kruger, K. 2008. Automated stock trading : a multi-agent, evolutionary approach. University of Cape Town. en_ZA
dc.identifier.uri http://hdl.handle.net/11427/14759
dc.description Includes bibliographical references (leaves 125-130). en_ZA
dc.description.abstract Stock market trading has garnered much interest over the past few decades as it has been made easier for the general public to trade. It is certainly an avenue for wealth growth, but like all risky undertakings, it must be understood for one to be consistently successful. There are, however, too many factors that influence it for one to make completely confident predictions. Automated computer trading has therefore been championed as a potential solution to this problem and is used in major brokerage houses world-wide. In fact, a third of all EU and US stock trades in 2006 were driven by computer algorithms. In this thesis we look at the challenges posed by the automatic generation of stock trading rules and portfolio management. We explore the viability of evolutionary algorithms, including genetic algorithms and genetic programming, for this problem and introduce an agent-based learning framework for individual and social intelligence that is applicable to general stock markets. Statistical tests were applied to determine whether or not there was a significant difference between the evolutionary trading approach and an accepted benchmark. It was found that while the evolutionary trading agents comfortably realised higher portfolio values than the ALSI, there was insufficient evidence to suggest that the agents outperformed the ALSI in terms of portfolio performance. Additionally, it was observed that while the traders combined knowledge from the expert traders to form complex trading models, these models did not result in any statistically significant positive returns. It must be said, however, that there was overwhelming evidence to suggest that the traders learned rules that were highly successful in predicting stock movement. en_ZA
dc.language.iso eng en_ZA
dc.subject.other Computer Science en_ZA
dc.title Automated stock trading : a multi-agent, evolutionary approach en_ZA
dc.type Master Thesis
uct.type.publication Research en_ZA
uct.type.resource Thesis en_ZA
dc.publisher.institution University of Cape Town
dc.publisher.faculty Faculty of Science en_ZA
dc.publisher.department Department of Computer Science en_ZA
dc.type.qualificationlevel Masters
dc.type.qualificationname MSc en_ZA
uct.type.filetype Text
uct.type.filetype Image
dc.identifier.apacitation Kruger, K. (2008). <i>Automated stock trading : a multi-agent, evolutionary approach</i>. (Thesis). University of Cape Town ,Faculty of Science ,Department of Computer Science. Retrieved from http://hdl.handle.net/11427/14759 en_ZA
dc.identifier.chicagocitation Kruger, Kurt. <i>"Automated stock trading : a multi-agent, evolutionary approach."</i> Thesis., University of Cape Town ,Faculty of Science ,Department of Computer Science, 2008. http://hdl.handle.net/11427/14759 en_ZA
dc.identifier.vancouvercitation Kruger K. Automated stock trading : a multi-agent, evolutionary approach. [Thesis]. University of Cape Town ,Faculty of Science ,Department of Computer Science, 2008 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/14759 en_ZA
dc.identifier.ris TY - Thesis / Dissertation AU - Kruger, Kurt AB - Stock market trading has garnered much interest over the past few decades as it has been made easier for the general public to trade. It is certainly an avenue for wealth growth, but like all risky undertakings, it must be understood for one to be consistently successful. There are, however, too many factors that influence it for one to make completely confident predictions. Automated computer trading has therefore been championed as a potential solution to this problem and is used in major brokerage houses world-wide. In fact, a third of all EU and US stock trades in 2006 were driven by computer algorithms. In this thesis we look at the challenges posed by the automatic generation of stock trading rules and portfolio management. We explore the viability of evolutionary algorithms, including genetic algorithms and genetic programming, for this problem and introduce an agent-based learning framework for individual and social intelligence that is applicable to general stock markets. Statistical tests were applied to determine whether or not there was a significant difference between the evolutionary trading approach and an accepted benchmark. It was found that while the evolutionary trading agents comfortably realised higher portfolio values than the ALSI, there was insufficient evidence to suggest that the agents outperformed the ALSI in terms of portfolio performance. Additionally, it was observed that while the traders combined knowledge from the expert traders to form complex trading models, these models did not result in any statistically significant positive returns. It must be said, however, that there was overwhelming evidence to suggest that the traders learned rules that were highly successful in predicting stock movement. DA - 2008 DB - OpenUCT DP - University of Cape Town LK - https://open.uct.ac.za PB - University of Cape Town PY - 2008 T1 - Automated stock trading : a multi-agent, evolutionary approach TI - Automated stock trading : a multi-agent, evolutionary approach UR - http://hdl.handle.net/11427/14759 ER - en_ZA


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