Forecasting stock market movement direction with support vector machine
Computers and Operations Research
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Mathematics and Computers in Simulation
A new approach to the rule-base evidential reasoning: Stock trading expert system application
Expert Systems with Applications: An International Journal
Hybrid approaches and dimensionality reduction for portfolio selection with cardinality constraints
IEEE Computational Intelligence Magazine
A stock trading expert system based on the rule-base evidential reasoning using Level 2 Quotes
Expert Systems with Applications: An International Journal
A Double-Deck Elevator Group Supervisory Control System Using Genetic Network Programming
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
An introduction to simulated evolutionary optimization
IEEE Transactions on Neural Networks
Performance evaluation of microbial fuel cell by artificial intelligence methods
Expert Systems with Applications: An International Journal
| Hi-index | 12.05 |
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.