Applying Learning Fuzzy Classifier System in Stock Technical-Analysis

• Main Authors: Chih-Che Lee, 李志哲
• Other Authors: An-Pin Chen
• Format: Others
• Language: zh-TW
• Published: 2003
• Online Access: http://ndltd.ncl.edu.tw/handle/25343429002946536297

碩士 === 國立交通大學 === 資訊管理學程碩士班 === 91 === Learning classifier system is a special class of production systems first introduced by Holland and Reitman in 1978 and has been successfully used in a number of event-response problems. It is very suitable to describe a partially unknown environment and complex problems where it is very difficult to give a mathematical description and dynamic environment. In this research, we utilized the learning fuzzy classifier systems(LFCS) that learn the stock price patterns for forecasting stock price. The objective of this research is to design the stock forecasting model that is able to create and refine its rules in response to observe performance and changes in the dynamic environment. We use six turning points of 10 moving average to represent the conditions and seventh turning point of 10 moving average to represent the action, and the comparison with single LFCS model over multiple LFCS model that analysis the performance. The empirical evidence shows that MLFCS outperform more than that SLFCS .The further researches can be extended the model with other technical-indexs and trading volume.