Optimization of control parameters for genetic algorithms
IEEE Transactions on Systems, Man and Cybernetics
Outline for a Logical Theory of Adaptive Systems
Journal of the ACM (JACM)
Construction of fuzzy systems using least-squares method and genetic algorithm
Fuzzy Sets and Systems - Theme: Modeling and control
Genetic algorithm for the personnel assignment problem with multiple objectives
Information Sciences: an International Journal
An adaptive recurrent fuzzy system for nonlinear identification
Applied Soft Computing
Population variation in genetic programming
Information Sciences: an International Journal
Adaptive fuzzy control for a class of uncertain nonaffine nonlinear systems
Information Sciences: an International Journal
Fuzzy classifier design using genetic algorithms
Pattern Recognition
Intelligent Threshhold Garch Model Applied to Stock Market of Transmissions that Volatility
ICCIT '07 Proceedings of the 2007 International Conference on Convergence Information Technology
Fuzzy risk analysis based on fuzzy numbers with different shapes and different deviations
Expert Systems with Applications: An International Journal
Adaptive controller with fuzzy rules emulated structure and its applications
Engineering Applications of Artificial Intelligence
Integration of genetic fuzzy systems and artificial neural networks for stock price forecasting
Knowledge-Based Systems
Save the best for last? The treatment of dominant predictors in financial forecasting
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
In this paper, we derive a new application of fuzzy systems designed for a generalized autoregression conditional heteroscedasticity (GARCH) model. In general, stock market performance is time-varying and nonlinear, and exhibits properties of clustering. The latter means simply that certain large changes tend to follow other large changes, and in general small changes tend to follow other small changes. This paper shows results from using the method of functional fuzzy systems to analyze the clustering in the case of a GARCH model. The optimal parameters of the fuzzy membership functions and GARCH model are extracted using a genetic algorithm (GA). The GA method aims to achieve a global optimal solution with a fast convergence rate for this fuzzy GARCH model estimation problem. From the simulation results, we have determined that the performance is significantly improved if the leverage effect of clustering is considered in the GARCH model. The simulations use stock market data from the Taiwan weighted index (Taiwan) and the NASDAQ composite index (NASDAQ) to illustrate the performance of the proposed method.