Applying two-stage XCS model on global overnight effect for local stock prediction

Authors:
An-Pin Chen;Yi-Chang Chen;Yu-Hua Huang
Affiliations:
Institute of Information Management, National Chiao Tung University, Hsinchu, Taiwan, Hsinchu, Taiwan, R.O.C.;Institute of Information Management, National Chiao Tung University, Hsinchu, Taiwan, Hsinchu, Taiwan, R.O.C.;Institute of Information Management, National Chiao Tung University, Hsinchu, Taiwan, Hsinchu, Taiwan, R.O.C.
Venue:
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part I
Year:
2005

Citing 2
Cited 0

Classifier systems and economic modeling

APL '96 Proceedings of the conference on Designing the future
Classifier fitness based on accuracy

Evolutionary Computation

Quantified Score

Hi-index	0.00

Visualization

Abstract

This study applied an integrated artificial intelligence method, extend learning classifier system (XCS), to predict the stock trend fluctuation considering the global overnight effect. However, some researchers have already indicated that XCS model that is applied successfully to form a forecast model in local market. Based on those prediction models, we put more effort to focus on the financial phenomenon, overnight effect between each two global markets, and we developed a two-stage XCS model to forecast the local stock market. In the experiments, DJi and Twi are chosen as referent and predicted markets respectively, and the model is trained by their historical data. For its accuracy verified, the model is tested by recently data. Finally, we have concluded that the proposed model successfully simulates the phenomenon, and the high ratio of correctness is definitely figured out.