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postgraduate thesis: Complex stock trading strategy based on parallel particle swarm optimization

TitleComplex stock trading strategy based on parallel particle swarm optimization
Authors
Advisors
Issue Date2012
PublisherThe University of Hong Kong (Pokfulam, Hong Kong)
Citation
Wang, F. [王緋]. (2012). Complex stock trading strategy based on parallel particle swarm optimization. (Thesis). University of Hong Kong, Pokfulam, Hong Kong SAR. Retrieved from http://dx.doi.org/10.5353/th_b4985888
AbstractTrading rules have been utilized in the stock market to make profit for more than a century. However, only using a single trading rule may not be sufficient to predict the stock price trend accurately. Although some complex trading strategies combining various classes of trading rules have been proposed in the literature, they often pick only one rule for each class, which may lose valuable information from other rules in the same class. In this thesis, a complex stock trading strategy, namely Performance-based Reward Strategy (PRS), is proposed. PRS combines the seven most popular classes of trading rules in financial markets, and for each class of trading rule, PRS includes various combinations of the rule parameters to produce a universe of 1059 component trading rules in all. Each component rule is assigned a starting weight and a reward/penalty mechanism based on profit is proposed to update these rules’ weights over time. To determine the best parameter values of PRS, we employ an improved time variant Particle Swarm Optimization (PSO) algorithm with the objective of maximizing the annual net profit generated by PRS. Due to the large number of component rules and swarm size, the optimization time is significant. A parallel PSO based on Hadoop, an open source parallel programming model of MapReduce, is employed to optimize PRS more efficiently. By omitting the traditional reduce phase of MapReduce, the proposed parallel PSO avoids the I/O cost of intermediate data and gets higher speedup ratio than previous parallel PSO based on MapReduce. After being optimized in an eight years training period, PRS is tested on an out-of-sample data set. The experimental results show that PRS outperforms all of the component rules in the testing period.
DegreeMaster of Philosophy
SubjectInvestments - Data processing.
Stocks - Data processing.
Parallel processing (Electronic computers)
Mathematical optimization.
Swarm intelligence.
Dept/ProgramComputer Science
Persistent Identifierhttp://hdl.handle.net/10722/181887

 

DC FieldValueLanguage
dc.contributor.advisorCheung, DWL-
dc.contributor.advisorYu, PLH-
dc.contributor.authorWang, Fei-
dc.contributor.author王緋-
dc.date.accessioned2013-03-20T06:29:52Z-
dc.date.available2013-03-20T06:29:52Z-
dc.date.issued2012-
dc.identifier.citationWang, F. [王緋]. (2012). Complex stock trading strategy based on parallel particle swarm optimization. (Thesis). University of Hong Kong, Pokfulam, Hong Kong SAR. Retrieved from http://dx.doi.org/10.5353/th_b4985888-
dc.identifier.urihttp://hdl.handle.net/10722/181887-
dc.description.abstractTrading rules have been utilized in the stock market to make profit for more than a century. However, only using a single trading rule may not be sufficient to predict the stock price trend accurately. Although some complex trading strategies combining various classes of trading rules have been proposed in the literature, they often pick only one rule for each class, which may lose valuable information from other rules in the same class. In this thesis, a complex stock trading strategy, namely Performance-based Reward Strategy (PRS), is proposed. PRS combines the seven most popular classes of trading rules in financial markets, and for each class of trading rule, PRS includes various combinations of the rule parameters to produce a universe of 1059 component trading rules in all. Each component rule is assigned a starting weight and a reward/penalty mechanism based on profit is proposed to update these rules’ weights over time. To determine the best parameter values of PRS, we employ an improved time variant Particle Swarm Optimization (PSO) algorithm with the objective of maximizing the annual net profit generated by PRS. Due to the large number of component rules and swarm size, the optimization time is significant. A parallel PSO based on Hadoop, an open source parallel programming model of MapReduce, is employed to optimize PRS more efficiently. By omitting the traditional reduce phase of MapReduce, the proposed parallel PSO avoids the I/O cost of intermediate data and gets higher speedup ratio than previous parallel PSO based on MapReduce. After being optimized in an eight years training period, PRS is tested on an out-of-sample data set. The experimental results show that PRS outperforms all of the component rules in the testing period.-
dc.languageeng-
dc.publisherThe University of Hong Kong (Pokfulam, Hong Kong)-
dc.relation.ispartofHKU Theses Online (HKUTO)-
dc.rightsThe author retains all proprietary rights, (such as patent rights) and the right to use in future works.-
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.source.urihttp://hub.hku.hk/bib/B49858889-
dc.subject.lcshInvestments - Data processing.-
dc.subject.lcshStocks - Data processing.-
dc.subject.lcshParallel processing (Electronic computers)-
dc.subject.lcshMathematical optimization.-
dc.subject.lcshSwarm intelligence.-
dc.titleComplex stock trading strategy based on parallel particle swarm optimization-
dc.typePG_Thesis-
dc.identifier.hkulb4985888-
dc.description.thesisnameMaster of Philosophy-
dc.description.thesislevelMaster-
dc.description.thesisdisciplineComputer Science-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.5353/th_b4985888-
dc.date.hkucongregation2013-

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