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
相关论文
共 50 条
  • [41] Multiobjective Optimization using Genetic Programming: Reducing Selection Pressure by Approximate Dominance
    Elkasaby, Ayman
    Salah, Akram
    Elfeky, Ehab
    PROCEEDINGS OF THE 6TH INTERNATIONAL CONFERENCE ON OPERATIONS RESEARCH AND ENTERPRISE SYSTEMS (ICORES), 2017, : 424 - 429
  • [42] EVALUATION OF INVESTMENT STRATEGIES CREATED BY MULTIOBJECTIVE GENETIC PROGRAMMING
    Jakubeci, Martin
    FINANCE AND PERFORMANCE OF FIRMS IN SCIENCE, EDUCATION, AND PRACTICE, 2015, : 498 - 509
  • [43] Constant optimization and feature standardization in multiobjective genetic programming
    Rockett, Peter
    GENETIC PROGRAMMING AND EVOLVABLE MACHINES, 2022, 23 (01) : 37 - 69
  • [44] Multiobjective evolution programming method for feeder reconfiguration
    Hsiao, YT
    IEEE TRANSACTIONS ON POWER SYSTEMS, 2004, 19 (01) : 594 - 599
  • [45] A Comparison of three evolutionary strategies for multiobjective genetic programming
    Yang Zhang
    Peter Rockett
    Artificial Intelligence Review, 2007, 27 : 149 - 163
  • [46] The Roles of Diversity Preservation and Mutation in Preventing Population Collapse in Multiobjective Genetic Programming
    Badran, Khaled M. S.
    Rockett, Peter I.
    GECCO 2007: GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE, VOL 1 AND 2, 2007, : 1551 - 1557
  • [47] An intelligent modeling method based on Genetic Programming and Genetic Algorithm
    Hu Jie
    Zhang Xi-nong
    Xie Shi-lin
    ADVANCES IN FRACTURE AND MATERIALS BEHAVIOR, PTS 1 AND 2, 2008, 33-37 : 795 - 800
  • [48] Method of image recognition based on genetic programming
    Yuan Hongbo
    Cai Zhenjiang
    Cheng Man
    Gao Liai
    ISTM/2007: 7TH INTERNATIONAL SYMPOSIUM ON TEST AND MEASUREMENT, VOLS 1-7, CONFERENCE PROCEEDINGS, 2007, : 1099 - 1102
  • [49] Multiobjective grammar-based genetic programming applied to the study of asthma and allergy epidemiology
    Veiga, Rafael V.
    Barbosa, Helio J. C.
    Bernardino, Heder S.
    Freitas, Joao M.
    Feitosa, Caroline A.
    Matos, Sheila M. A.
    Alcantara-Neves, Neuza M.
    Barreto, Mauricio L.
    BMC BIOINFORMATICS, 2018, 19
  • [50] Multiobjective grammar-based genetic programming applied to the study of asthma and allergy epidemiology
    Rafael V. Veiga
    Helio J. C. Barbosa
    Heder S. Bernardino
    João M. Freitas
    Caroline A. Feitosa
    Sheila M. A. Matos
    Neuza M. Alcântara-Neves
    Maurício L. Barreto
    BMC Bioinformatics, 19
12345