Incremental genetic fuzzy expert trading system for derivatives market timing

H. S. Ng, K. P. Lam, S. S. Lam

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

11 Citations (Scopus)

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.

Original languageEnglish
Title of host publication2003 IEEE International Conference on Computational Intelligence for Financial Engineering, CIFEr 2003 - Proceedings
Pages421-427
Number of pages7
ISBN (Electronic)0780376544
DOIs
Publication statusPublished - 2003
Event2003 IEEE International Conference on Computational Intelligence for Financial Engineering, CIFEr 2003 - Hong Kong, China
Duration: 20 Mar 200323 Mar 2003

Publication series

NameIEEE/IAFE Conference on Computational Intelligence for Financial Engineering, Proceedings (CIFEr)
Volume2003-January

Conference

Conference2003 IEEE International Conference on Computational Intelligence for Financial Engineering, CIFEr 2003
Country/TerritoryChina
CityHong Kong
Period20/03/0323/03/03

Keywords

  • Fuzzy systems
  • Genetic algorithms
  • Hybrid intelligent systems
  • Modeling
  • Monitoring
  • Research and development management
  • Signal design
  • Stock markets
  • Systems engineering and theory
  • Timing

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