Forecasting the Stock Market with Linguistic Rules Generated from the Minimize Entropy Principle and the Cumulative Probability Distribution Approaches

Entropy

Chung-Ho Su²  Tai-Liang Chen¹  Ching-Hsue Cheng² 

[1] Department of Information Management and Communication, Wenzao Ursuline College of Languages, 900, Mintsu 1st Road, Kaohsiung 807, Taiwan;Department of Information Management, National Yunlin University of Science and Technology,123, section 3, University Road, Touliu, Yunlin 640, Taiwan; E-Mails:

关键词: minimize entropy principle approach;    cumulative probability distribution approach;    rough set theory;    stock market forecasting;   

DOI  :  10.3390/e12122397

来源: mdpi

【 摘 要 】

To forecast a complex and non-linear system, such as a stock market, advanced artificial intelligence algorithms, like neural networks (NNs) and genetic algorithms (GAs) have been proposed as new approaches. However, for the average stock investor, two major disadvantages are argued against these advanced algorithms: (1) the rules generated by NNs and GAs are difficult to apply in investment decisions; and (2) the time complexity of the algorithms to produce forecasting outcomes is very high. Therefore, to provide understandable rules for investors and to reduce the time complexity of forecasting algorithms, this paper proposes a novel model for the forecasting process, which combines two granulating methods (the minimize entropy principle approach and the cumulative probability distribution approach) and a rough set algorithm. The model verification demonstrates that the proposed model surpasses the three listed conventional fuzzy time-series models and a multiple regression model (MLR) in forecast accuracy.

【 授权许可】

CC BY   
© 2010 by the authors; licensee MDPI, Basel, Switzerland.