Analyze Long & Mid-term Trends of Stock with Genetic Programming on Moving Average and Turning Points

摘要：

This paper employs Genetic Programming (GP) with individuals of tree structure to form empirical formulas in order to track the dynamic pattern of the moving average curves of stock prices. We fmd that our metbod tracks the 60 day moving average better than other shorter period averages. In order to minimize the effects of noise and other random events impacting on the markets and maximize the effective information abstracted from the origin data, two comparable data preprocessing methods for turning points are proposed to cooperate with GP for more stable long & mid-term dynamic analysis and prediction. We use either discrete data with fixed time intervals as long as 120 days or data at local extreme by FFT. So, the formula finding system tracks the next turning point with the information of several previous turning points. Simulations show that our method to track and predict long & mid-term change trend of stock price is practical.

关键词： Genetic Programming Fitness function Moving average Fast Fourier Transformation (FFT) filtering

作者: Erbo Zhao Zhangang Han

作者单位: Systems Science Department, Management School Beijing Normal University Beijing,China Systems Science Department,Management School Beijing Normal University Beijing,China

会议类型: 国际会议

会议名称: The 2nd IEEE International Conference on Advanced Computer Control(第二届先进计算机控制国际会议 ICACC 2010)

会议地点: 沈阳

会议语种:英文

页码: 87-91

在线出版日期: 2010-03-27（万方平台首次上网日期，不代表论文的发表时间）