A Novel Method to Study Stock Market Trend Based on Combined Forecasting

Compared with econometric models, which require numerous hypotheses and suffer various other limitations, artificial intelligence models are more flexible, able to solve any nonlinear problems, and more suitable for analyzing dynamic environments such as stock markets. This study combines two artificial intelligence technologies and attempts to use genetic theories to produce a rule set, which can be adapted to stock market behaviors. And re-learns it to refine those rules, hopefully discovering knowledge hidden in the stock market and to establish a XCS-Neural-Network based trading system, and this system is then used to identify environmental patterns and predict the values of the test set. Experiments reveal that all test data in this study have accuracy rates exceeding 62.33%. Therefore, this study confidently concludes that the proposed system can help investors make more precise investment decisions.