Incremental genetic fuzzy expert trading system for derivatives market timing
Refereed conference paper presented and published in conference proceedings

CUHK Authors
Author(s) no longer affiliated with CUHK


Times Cited
Web of Science4WOS source URL (as at 30/07/2020) Click here for the latest count
Altmetrics Information
.

Other information
AbstractTechnical 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.
All Author(s) ListNg HS, Lam KP, Lam SS
Name of ConferenceIEEE International Conference on Computational Intelligence for Financial Engineering
Start Date of Conference20/03/2003
End Date of Conference23/03/2003
Place of ConferenceHONG KONG
Country/Region of ConferenceChina
Year2003
Month1
Day1
PublisherIEEE
Pages421 - 427
ISBN0-7803-7654-4
LanguagesEnglish-United Kingdom
Keywordsfuzzy system; genetic algorithm; market timing; technical indicator
Web of Science Subject CategoriesBusiness & Economics; Business, Finance; Computer Science; Computer Science, Artificial Intelligence; Economics; Mathematics; Statistics & Probability

Last updated on 2020-31-07 at 00:39