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

Examensarbete för masterexamen

Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.12380/178500
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Type: Examensarbete för masterexamen
Master Thesis
Title: Finding Predictive Patterns in Historical Stock Data Using Self-Organizing Maps and Particle Swarm Optimization
Authors: Salomonsson, Hans
Abstract: The 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.
Keywords: Fysik;Physical Sciences
Issue Date: 2012
Publisher: Chalmers tekniska högskola / Institutionen för energi och miljö
Chalmers University of Technology / Department of Energy and Environment
Series/Report no.: Rapportserie för Avdelningen för fysisk resursteori : 2012:13
URI: https://hdl.handle.net/20.500.12380/178500
Collection:Examensarbeten för masterexamen // Master Theses



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