Exploring Stock Market Dynamism by Applying Dynamic Fuzzy Model in Combination with Support Vector Machine

• Main Authors: Chen Ping Jie, 陳炳傑
• Other Authors: Deng-Yiv Chiu
• Format: Others
• Language: zh-TW
• Published: 2006
• Online Access: http://ndltd.ncl.edu.tw/handle/75934437255229667721

碩士 === 中華大學 === 資訊管理學系 === 94 === In the study, a new dynamic fuzzy model is proposed in combination with support vector machine (SVM) to forecast stock market dynamism. In this new integrated model, the fuzzy model integrates various influence factors 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. To evaluate the performance of the new integrated model, we compare it with the traditional forecast methods and design different experiments to testify. From the experiment results, the model from the study does generate better accuracy in forecast than other forecast models.