Paper Info
Title
Enhanced decision making mechanism of rule-based genetic network programming for creating stock trading signals
Abstract
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.
Year
DOI
Venue
2013
10.1016/j.eswa.2013.05.037
Expert Syst. Appl.
Keywords
Field
DocType
rule accumulation,optimal action rule,rule-based genetic network programming,stock trading,optimal individual,rule generator,rule extraction,conventional individual-based trading model,large number,rule pool,rule-based trading model,enhanced decision,stock trading signal,evolutionary computation,technical analysis
Decision rule,Rule-based system,Evolutionary algorithm,Computer science,Evolutionary computation,Stock exchange,Inventory control,Artificial intelligence,Genetic algorithm,Technical analysis
Journal
Volume
Issue
ISSN
40
16
0957-4174
Citations 
PageRank 
References 
16
0.68
12
Authors
4
Name
Order
Citations
PageRank
Shingo Mabu149377.00
Kotaro Hirasawa2704113.11
Masanao Obayashi319826.10
Takashi Kuremoto419627.73