Title :
Features extraction based on particle swarm optimization for high frequency financial data
Author :
Wang, Ting-Liang ; Wang, Min
Author_Institution :
Sch. of Econ. & Manage., Beihang Univ., Beijing, China
Abstract :
A novel stock trading system is developed in this paper. The trading system combines particle swarm optimization based clustering method and basic financial rules to discover the potential features of high frequency financial data. By analyzing the robustness of PSO based trading system and comparing with the classical buy and hold trading policy, the empirical study gives evidences that the newly proposed trading system can be used as a decision support system for stock investors.
Keywords :
decision support systems; feature extraction; financial data processing; particle swarm optimisation; pattern clustering; stock markets; PSO based trading system; clustering method; decision support system; feature extraction; financial rules; high frequency financial data; particle swarm optimization; stock investment; stock trading system; trading policy; Algorithm design and analysis; Clustering algorithms; Clustering methods; Indexes; Particle swarm optimization; Partitioning algorithms; Time series analysis; clustering; high frequency financial data; particle swarm optimization; trading rule;
Conference_Titel :
Granular Computing (GrC), 2011 IEEE International Conference on
Conference_Location :
Kaohsiung
Print_ISBN :
978-1-4577-0372-0
DOI :
10.1109/GRC.2011.6122688
