Maximizing winning trades using a rough set based other-product (RSPOP) fuzzy neural network intelligent stock trading system

Author

Tan, Andy ; Quek, C.

Author_Institution

Centre of Comput. Intelligence, Nanyang Technol. Univ., Singapore

Volume

3

fYear

2005

fDate

2-5 Sept. 2005

Firstpage

2076

Abstract

Trading systems have been relying more and more on the use of novel computational intelligence techniques in the formulation of trading decisions. A novel RSPOP intelligent stock trading system is proposed in this paper. This trading system is demonstrated empirically to achieve significantly superior returns on live stock data, and is able to filter out erroneous trading signals generated by the moving average trading rule. This ability to filter out erroneous signals is measured by the percentage of winning trades. The trading system is demonstrated empirically to achieve more than 92% of winning trades compared to an average of 70% of winning trades demonstrated by the conventional trading system based on the moving average trading rule.

Keywords

fuzzy set theory; fuzzy systems; neural nets; rough set theory; stock markets; computational intelligence techniques; fuzzy neural network; intelligent stock trading system; live stock data; moving average trading rule; rough set based other-product; trading decisions; Competitive intelligence; Computational intelligence; Counting circuits; Filters; Fuzzy neural networks; Intelligent networks; Intelligent systems; Investments; Signal generators; Stock markets;

fLanguage

English

Publisher

ieee

Conference_Titel

Evolutionary Computation, 2005. The 2005 IEEE Congress on

Print_ISBN

0-7803-9363-5

Type

conf

DOI

10.1109/CEC.2005.1554951

Filename

1554951