Investment using evolutionary learning methods and technical rules

Massimiliano Kaucic

European Journal of Operational Research, 2010, vol. 207, issue 3, 1717-1727

Abstract: In this paper, I propose a genetic learning approach to generate technical trading systems for stock timing. The most informative technical indicators are selected from a set of almost 5000 signals by a multi-objective genetic algorithm with variable string length. Successively, these signals are combined into a unique trading signal by a learning method. I test the expert weighting solution obtained by the plurality voting committee, the Bayesian model averaging and Boosting procedures with data from the S&P 500 Composite Index, in three market phases, up-trend, down-trend and sideways-movements, covering the period 2000-2006. Computational results indicate that the near-optimal set of rules varies among market phases but presents stable results and is able to reduce or eliminate losses in down-trend periods.

Keywords: Genetic; algorithms; Evolutionary; learning; Technical; analysis; Expert; trading; system (search for similar items in EconPapers)
Date: 2010
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Citations: View citations in EconPapers (9)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:207:y:2010:i:3:p:1717-1727

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