Title :
Trading rules for high-frequency financial data based on hybrid clustering
Author :
Han-Min Ye ; Min Wang ; Ting-Liang Wang
Author_Institution :
Sch. of Inf. Sci. & Technol., Guilin Univ. of Technol., Guilin, China
Abstract :
A novel stock trading rule is proposed in this paper. The trading rule combines self-organizing map network and K-means clustering algorithm to discover the potential features of high frequency financial data at first, and then new trading strategies are proposed to assist investors´ transaction decision. The hybrid clustering based trading rule is applied to Hushen 300 Index of Chain, and compared its performances with other existing classical trading technique. Empirical research results give evidence that the newly proposed trading rule can be used to discover intraday patterns, and provide decision support to help stock investors gain more returns.
Keywords :
financial data processing; pattern clustering; self-organising feature maps; stock markets; China; Hushen 300 Index; K-means clustering algorithm; high-frequency financial data; hybrid clustering; investor transaction decision; self-organizing map network; stock investment; stock trading rule; Clustering algorithms; Educational institutions; Indexes; Neurons; Stock markets; Strontium; Training; High frequency financial data; K-means; Self-organizing maps; Trading rules;
Conference_Titel :
Fuzzy Systems and Knowledge Discovery (FSKD), 2011 Eighth International Conference on
Conference_Location :
Shanghai
Print_ISBN :
978-1-61284-180-9
DOI :
10.1109/FSKD.2011.6019696
