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.
['Hanmin Ye', 'Min Wang', 'Ting-Liang Wang']
Trading rules for high-frequency financial data based on hybrid clustering