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

被引：17
|
作者：
Pimenta, Alexandre [1 ,2 ]
Nametala, Ciniro A. L. [1 ,2 ]
Guimaraes, Frederico G. [3 ]
Carrano, Eduardo G. [3 ]
机构：
[1] Inst Fed Minas Gerais, Dept Comp, Formiga, MG, Brazil
[2] Univ Fed Minas Gerais, Grad Program Elect Engn, Belo Horizonte, MG, Brazil
[3] Univ Fed Minas Gerais, Dept Elect Engn, Belo Horizonte, MG, Brazil
来源：
COMPUTATIONAL ECONOMICS | 2018年 / 52卷 / 01期
关键词：
Genetic programming; Multiobjective optimization; Technical analysis; Stock exchange market; Feature selection; BOVESPA; ALGORITHMS;
D O I：
10.1007/s10614-017-9665-9
中图分类号：
F [经济];
学科分类号：
02 ;
摘要：
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.
引用
收藏
页码：125 / 144
页数：20
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