A hybrid genetic-neural architecture for stock indexes forecasting
Information Sciences: an International Journal - Special issue: Computational intelligence in economics and finance
Mining stock market tendency using GA-Based support vector machines
WINE'05 Proceedings of the First international conference on Internet and Network Economics
The hybrid forecasting model based on chaotic mapping, genetic algorithm and support vector machine
Expert Systems with Applications: An International Journal
Regression application based on fuzzy ν-support vector machine in symmetric triangular fuzzy space
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Improving trading systems using the RSI financial indicator and neural networks
PKAW'10 Proceedings of the 11th international conference on Knowledge management and acquisition for smart systems and services
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
The forecasting model based on fuzzy novel ν-support vector machine
Expert Systems with Applications: An International Journal
Stock price prediction based on procedural neural networks
Advances in Artificial Neural Systems
A hybrid stock selection model using genetic algorithms and support vector regression
Applied Soft Computing
Expert Systems with Applications: An International Journal
Hi-index | 12.06 |
In the study, a new dynamic fuzzy model is proposed in combination with support vector machine (SVM) to explore stock market dynamism. The fuzzy model integrates various factors with influential degree as the input variables, and the genetic algorithm (GA) adjusts the influential degree of each input variable dynamically. SVM then serves to predict stock market dynamism in the next phase. In the meanwhile, the multiperiod experiment method is designed to simulate the volatility of stock market. The input variables in the study include a total of 61 variables, including technical indicators in stock market, technical indicators in futures market, and the macroeconomic variables. To evaluate the performance of the new integrated model, we compare it with the traditional forecast methods and design different experiments to testify. In the experiment results, the model from the study does generate better accuracy rate than others.