Finding Predictive Patterns in Historical Stock Data Using Self-Organizing Maps and Particle Swarm Optimization

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dc.contributor.authorSalomonsson, Hans
dc.contributor.departmentChalmers tekniska högskola / Institutionen för energi och miljösv
dc.contributor.departmentChalmers University of Technology / Department of Energy and Environmenten
dc.date.accessioned2019-07-03T13:10:39Z
dc.date.available2019-07-03T13:10:39Z
dc.date.issued2012
dc.description.abstractThe purpose of this thesis is to find a new methodology for finding predictive patterns in candlestick charts without any predefining of how these might look. An algorithm combining particle swarm optimization and self-organizing map has been implemented and evaluated. Non-transformed daily open, high, low and close data has been used as input. The algorithm found predictive patterns that statistically significant outperformed random trading. Moreover, interesting properties such as the optimal length of the pattern, target length and similarity of input to found pattern are discussed.
dc.identifier.urihttps://hdl.handle.net/20.500.12380/178500
dc.language.isoeng
dc.relation.ispartofseriesRapportserie för Avdelningen för fysisk resursteori : 2012:13
dc.setspec.uppsokLifeEarthScience
dc.subjectFysik
dc.subjectPhysical Sciences
dc.titleFinding Predictive Patterns in Historical Stock Data Using Self-Organizing Maps and Particle Swarm Optimization
dc.type.degreeExamensarbete för masterexamensv
dc.type.degreeMaster Thesisen
dc.type.uppsokH
local.programmeApplied physics (MPAPP), MSc
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