Applying a XCS-Neural-network Based Trading Model on Taiwan Stock Index Trend Forecasting

• Main Author: 阮榆方
• Other Authors: 陳安斌
• Format: Others
• Language: zh-TW
• Published: 2007
• Online Access: http://ndltd.ncl.edu.tw/handle/97883006939523520627

碩士 === 國立交通大學 === 資訊管理研究所 === 95 === Econometricians build precise hypotheses in advance when they use econometric models to discuss the changing trends in the stock market. But baffled by these unreasonable hypotheses, economics usually can’t explain real behaviors of stock markets very well with mathematical models. Therefore, this research tries to use genetic theories to produce the rule base adapting to the behaviors of stock markets, and then re-learn it to refine those rules, so that hopefully knowledge hidden in the stock market could be discovered. Artificial intelligence models are frequently used in financial analysis in recent years. Compared with the use of many hypotheses and limitations in econometric models, artificial intelligence models are more flexible, able to solve any nonlinear problems, and more suitable to analyze dynamic environments like stock markets. This research combines two Artificial intelligence technologies: extended classifier system and backpropagation neural network to construct a XCS-Neural-network Based Trading System, and we use this system to learn patterns from the environment and then predict values of the test set later. Experiments reveal that all test data in this research have accuracy rate 50% above. Therefore, we are confident to conclude that this system could help investors to make more precise investment decisions.