A genetic fuzzy expert system for stock market timing

Author

Lam, S.S.

Author_Institution

Sch. of Bus. & Adm., Open Univ. of Hong Kong, Kowloon, China

Volume

1

fYear

2001

fDate

2001

Firstpage

410

Abstract

Buying and selling of stocks is an essential activity in financial markets. Financial experts invented many market indicators to monitor the movement of stock prices. Trading rules were defined on these indicators for generating buy-sell signals. These rules are fuzzy in nature and they can predict to a certain extent but not always the price movement of stocks. Their accuracy is time-varying and it is impossible to have the best trading rule. A certain combination of trading rules will generate more reliable buy-sell signals for a particular stock in a certain period of time. The selection of these trading rules can be formulated as an optimization problem. We propose a new stock market timing system by integrating a genetic algorithm with a fuzzy expert system. A genetic algorithm is used to optimize the selection of fuzzy trading rules. Experiments were conducted to evaluate the performance of the system and the results indicate that the system can generate more reliable buy-sell signals even in a declining market

Keywords

expert systems; financial data processing; fuzzy logic; genetic algorithms; stock markets; uncertainty handling; experiments; financial markets; financial trading rules; genetic algorithm; genetic fuzzy expert system; optimization; stock market timing; stock prices; Art; Data mining; Fuzzy logic; Genetic algorithms; Humans; Hybrid intelligent systems; Monitoring; Signal generators; Stock markets; Timing;

fLanguage

English

Publisher

ieee

Conference_Titel

Evolutionary Computation, 2001. Proceedings of the 2001 Congress on

Conference_Location

Seoul

Print_ISBN

0-7803-6657-3

Type

conf

DOI

10.1109/CEC.2001.934420

Filename

934420