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

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,...

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Bibliographic Details
Published in:Computational economics 2018-06, Vol.52 (1), p.125-144
Main Authors: Pimenta, Alexandre, Nametala, Ciniro A. L., Guimarães, Frederico G., Carrano, Eduardo G.
Format: Article
Language:English
Subjects:
Arches
Artificial neural networks
Assets
Automation
Behavioral/Experimental Economics
Computer Appl. in Social and Behavioral Sciences
Economic Theory/Quantitative Economics/Mathematical Methods
Economics
Economics and Finance
Foreign exchange rates
Fuzzy logic
Fuzzy systems
Genetic algorithms
Market value
Markets
Math Applications in Computer Science
Multiple objective analysis
Neural networks
Operations Research/Decision Theory
Optimization
Profits
Securities markets
Stock exchanges
Time series
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Summary: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.
ISSN:0927-7099
1572-9974
DOI:10.1007/s10614-017-9665-9