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

fYear

2011

fDate

8-10 Nov. 2011

Firstpage

728

Lastpage

733

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;

fLanguage

English

Publisher

ieee

Conference_Titel

Granular Computing (GrC), 2011 IEEE International Conference on

Conference_Location

Kaohsiung

Print_ISBN

978-1-4577-0372-0

Type

conf

DOI

10.1109/GRC.2011.6122688

Filename

6122688