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A SAX-GA Approach to Evolve Investment Strategies on Financial Markets based on Pattern Discovery Techniques

Canelas, A. ; Neves, R. ; Horta, N.

Expert Systems with Applications Vol. , Nº , pp. - , January, 2013.

ISSN (print): 0957-4174
ISSN (online):

Journal Impact Factor: 2,249 (in 2014)

Digital Object Identifier: 10.1016/j.eswa.2012.09.002

Abstract
This paper presents a new computational finance approach, combining a Symbolic Aggregate approXimation (SAX) technique together with an optimization kernel based on genetic algorithms (GA). The SAX representation is used to describe the financial time series, so that, relevant patterns can be efficiently identified. The evolutionary optimization kernel is here used to identify the most relevant patterns and generate investment rules. The proposed approach was tested using real data from S&P500. The achieved results show that the proposed approach outperforms both B&H and other state-of-the-art solutions.