Fuzzy intertransaction class association rule mining using Genetic Network Programming for stock market prediction

Abstract

Abstract Intertransaction class association rule (interCAR) has the ability to find the relationships among attributes from different transactions, which has shown its effectiveness for stock market prediction. A crisp interCAR mining method based on Genetic Network Programming (GNP) has been studied in our previous work. But, the crisp method loses much useful information in the discretization and it has many unstable factors influencing the prediction results, so more information is desired in order to make the prediction safer and more efficient. In this paper, a fuzzy interCAR mining method is proposed to keep as much information as possible in the data transformation. Besides, the proposed method has ability that the trading actions bring large profits. The proposed method is applied to Tokyo Stock Exchange, where we compared it with the crisp method as well as some other methods. © 2011 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.