An Automated Investing Method for Stock Market Based on Multiobjective Genetic Programming

Alexandre Pimenta (), Ciniro A. L. Nametala (), Frederico G. Guimarães () and Eduardo G. Carrano ()
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Alexandre Pimenta: Instituto Federal Minas Gerais
Ciniro A. L. Nametala: Instituto Federal Minas Gerais
Frederico G. Guimarães: Universidade Federal de Minas Gerais (UFMG)
Eduardo G. Carrano: Universidade Federal de Minas Gerais (UFMG)

Computational Economics, 2018, vol. 52, issue 1, No 7, 125-144

Abstract: Abstract Stock market automated investing is an area of strong interest for the academia, casual, and professional investors. In addition to conventional market methods, various sophisticated techniques have been employed to deal with such a problem, such as ARCH/GARCH predictors, artificial neural networks, fuzzy logic, etc. A computational system that combines a conventional market method (technical analysis), genetic programming, and multiobjective optimization is proposed in this work. This system was tested in six historical time series of representative assets from Brazil stock exchange market (BOVESPA). The proposed method led to profits considerably higher than the variation of the assets in the period. The financial return was positive even in situations in which the share lost market value.

Keywords: Genetic programming; Multiobjective optimization; Technical analysis; Stock exchange market; Feature selection; BOVESPA (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s10614-017-9665-9

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