Evolutionary stock trading decision support system using sliding window

Author

Wang, Jung-Hua ; Chen, Shiuan-Ming

Author_Institution

Dept. of Electr. Eng., Nat. Taiwan Ocean Univ., Keelung, Taiwan

fYear

1998

fDate

4-9 May 1998

Firstpage

253

Lastpage

258

Abstract

A novel evolutionary decision support system (EDSS) that is effective for making profitable trading decisions in the stock market is presented. A genetic algorithm is incorporated with a sliding window scheme to effectively estimate the most profitable trading decision, namely buy, hold or sell. Because of its data-driven nature and the genetic change to the positive course, EDSS bypasses the complicated steps of network establishment and subsequent training, and it can directly deal with the problem of structural instability that plagues traditional rule-based systems. Empirical tests are conducted on the weighted price index of the Taiwan stock market (TSEWSI). Compared to the buy-and-hold strategy, the EDSS can achieve a significant improvement in profit gains

Keywords

decision support systems; electronic trading; genetic algorithms; stock markets; Taiwan Stock Exchange Weighted Share Index; Taiwan stock market; buy-and-hold strategy; data-driven nature; evolutionary decision support system; genetic algorithm; profit gains; profitable trading decisions; sliding window scheme; structural instability; weighted price index; Decision making; Decision support systems; Economic forecasting; Expert systems; Genetic algorithms; Hardware; Knowledge based systems; Oceans; Stock markets; Testing;

fLanguage

English

Publisher

ieee

Conference_Titel

Evolutionary Computation Proceedings, 1998. IEEE World Congress on Computational Intelligence., The 1998 IEEE International Conference on

Conference_Location

Anchorage, AK

Print_ISBN

0-7803-4869-9

Type

conf

DOI

10.1109/ICEC.1998.699721

Filename

699721