FCMAC-AARS: A Novel FNN Architecture for Stock Market Prediction and Trading

Author

Zaiyi, Guo ; Quek, Chai ; Maskell, Douglas L.

Author_Institution

Nanyang Technol. Univ., Singapore

fYear

0

fDate

0-0 0

Firstpage

2375

Lastpage

2381

Abstract

A novel fuzzy neural network architecture, the approximate analogical reasoning based fuzzy CMAC (FCMAC-AARS), is proposed. AARS is incorporated into the fuzzy CMAC structure as it is conceptually clearer and more computationally efficient than the CRI and TVR fuzzy inference schemes. A prediction and trading framework has been proposed which exploits the price percentage oscillator (PPO) for input preprocessing and trading decision making. Numerical experiments conducted on real-life stock data confirm the validity of the design and the performance of the FCMAC-AARS system.

Keywords

cerebellar model arithmetic computers; decision making; fuzzy reasoning; stock markets; analogical reasoning; decision making; fuzzy inference scheme; fuzzy neural network architecture; input preprocessing; price percentage oscillator; stock market prediction; stock market trading; Backpropagation algorithms; Computer architecture; Data preprocessing; Decision making; Decision support systems; Fuzzy neural networks; Neural networks; Oscillators; Predictive models; Stock markets;

fLanguage

English

Publisher

ieee

Conference_Titel

Evolutionary Computation, 2006. CEC 2006. IEEE Congress on

Conference_Location

Vancouver, BC

Print_ISBN

0-7803-9487-9

Type

conf

DOI

10.1109/CEC.2006.1688602

Filename

1688602