Using multi-stage data mining technique to build forecast model for Taiwan stocks
Taiwan stock market trend is fast changing. It is affected by not only the individual investors and the three major institutional investors, but also impacted by domestic political and economic situations. Therefore, to precisely grasp the stock market movement, one must build a perfect stock forecast model. In this article, we used a multi-stage optimized stock forecast model to grasp the changing trend of the stock market. First, data of 2 stocks, TSMC and UMC were collected, and then inputted the test data into the genetic programing and built a model to find out the arithmetic expressions. Artificial Fish Swarm Algorithm is used to dynamically adjust the variable factors and constant factors in the arithmetic expressions. Next, we took the error term (?) in arithmetic expressions to Gray Model Neural Network to make the forecast. Finally, we used the Artificial Fish Swarm Algorithm to dynamically adjust the parameters of the Gray Model Neural Network to enhance the precision of the stock forecast model as a whole. The result showed that the forecast capability of each stage after the optimization process is better than that of its previous stage, and the mixed stock forecast model (GPAFSA+GMNNAFSA) in stage 4 greatly enhanced the precision of the forecast.
data mining genetic programing arithmetic expressions artificial fish swarm algorithm grey model neural network