DocumentCode :
3414617
Title :
Incremental genetic fuzzy expert trading system for derivatives market timing
Author :
Ng, H.S. ; Lam, K.P. ; Lam, S.S.
Author_Institution :
Dept. of Syst. Eng. & Eng. Manage., Chinese Univ. of Hong Kong, Shatin, China
fYear :
2003
fDate :
20-23 March 2003
Firstpage :
421
Lastpage :
427
Abstract :
Technical indicators are normally used to monitor the stock prices and assist investors to set up trading rules to make the buy-sell-hold decision. Although some trading rules are clear, most of them are vague and fuzzy. Therefore, an investor cannot be the winner all the time with the same set of trading rules. The weight of trading rules should be varied with time. A Genetic Fuzzy Expert Trading System (GFETS) was designed to simulate the vague and fuzzy trading rules and give the buy-sell signal. Fuzzy trading rules are optimized and selected using genetic algorithm in GFETS. Experimental evaluations showed that trading with the optimized fuzzy trading rules obtains a good profitable return. To maintain the quality of the fuzzy trading rules being in-used, GFETS must be re-trained from time-to-time. In this paper, an incremental training approach was studied and evaluated with all Hang Seng China Enterprises Index (HSCEI) stocks. The risk and the profit return compared with other trading strategies were reported.
Keywords :
electronic trading; expert systems; fuzzy logic; genetic algorithms; learning (artificial intelligence); stock markets; uncertainty handling; Hang Seng China Enterprises Index stocks; buy-sell-hold decision; derivatives market timing; experimental evaluations; fuzzy trading rules; genetic algorithm; incremental genetic fuzzy expert trading system; incremental learning; profit return; risk; stock prices; trading rules; vague trading rules; Fuzzy systems; Genetic algorithms; Hybrid intelligent systems; Modeling; Monitoring; Research and development management; Signal design; Stock markets; Systems engineering and theory; Timing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computational Intelligence for Financial Engineering, 2003. Proceedings. 2003 IEEE International Conference on
Print_ISBN :
0-7803-7654-4
Type :
conf
DOI :
10.1109/CIFER.2003.1196291
Filename :
1196291
Link To Document :
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