Enhanced decision making mechanism of rule-based genetic network programming for creating stock trading signals

• Published in: Expert systems with applications 2013-11, Vol.40 (16), p.6311-6320
• Main Authors: Mabu, Shingo, Hirasawa, Kotaro, Obayashi, Masanao, Kuremoto, Takashi
• Format: Article
• Language: English
• Subjects: Algorithmics. Computability. Computer arithmetics; Applied sciences; Computer science; control theory; systems; Decision making; Decision theory. Utility theory; Evolutionary algorithms; Evolutionary computation; Exact sciences and technology; Genetic network programming; Genetics; Networks; Operational research and scientific management; Operational research. Management science; Optimization; Portfolio theory; Programming; Raw materials; Rule extraction; Stock trading; Technical analysis; Theoretical computing; Training
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2013.05.037

•A novel rule-based decision making mechanism for stock trading is proposed.•A large number of effective trading rules are generated by evolution and learning.•A classification algorithm for stock trading is proposed.•The results show that the rule-based system is better than individual-based method. Evolutionary computation generally aims to create the optimal individual which represents optimal action rules when it is applied to agent systems. Genetic Network Programming (GNP) has been proposed as one of the graph-based evolutionary computations in order to create optimal individuals. GNP with rule accumulation is an extended algorithm of GNP, which extracts a large number of rules throughout the generations and stores them in rule pools, which is different from general evolutionary computations. Concretely, the individuals of GNP with rule accumulation are regarded as evolving rule generators in the training phase and the generated rules in the rule pools are actually used for decision making. In this paper, GNP with rule accumulation is enhanced in terms of its rule extraction and classification abilities for generating stock trading signals considering up and down trends and occurrence frequency of specific buying/selling timing. A large number of buying and selling rules are extracted by the individuals evolved in the training period. Then, a unique classification mechanism is used to appropriately determine whether to buy or sell stocks based on the extracted rules. In the testing simulations, the stock trading is carried out using the extracted rules and it is confirmed that the rule-based trading model shows higher profits than the conventional individual-based trading model.