Stock market trading rule discovery using two-layer bias decision tree

被引:28
作者
Wang, JL
Chan, SH
机构
[1] Fortune Inst Technol, Dept Management Informat Syst, Kaohsiung 831, Taiwan
[2] Natl Kaohsiung First Univ Sci & Technol, Inst Management, Kaohsiung, Taiwan
关键词
artificial intelligence; rule discovery; stock market;
D O I
10.1016/j.eswa.2005.07.006
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
This study uses the daily stock prices of Microsoft, Intel, and IBM to assess stock market purchasing opportunities with simple technical indicators. This study used a two-layer bias decision tree. The methodology used in this study differs from that used in other studies in two respects. First, this study modified the decision model into the bias decision model to reduce the classification error. Second, this study used the two-layer bias decision tree to improve purchasing accuracy. The empirical results of this study not only improve purchasing accuracy and investment returns, but also have the advantages of fast learning speed, robustness, simplicity, stability, and generality. (c) 2005 Elsevier Ltd. All rights reserved.
引用
收藏
页码:605 / 611
页数:7
相关论文
共 18 条
[1]  
[Anonymous], 1986, Technical analysis of the futures markets: A comprehensive guide to trading methods and applications
[2]   An empirical comparison of voting classification algorithms: Bagging, boosting, and variants [J].
Bauer, E ;
Kohavi, R .
MACHINE LEARNING, 1999, 36 (1-2) :105-139
[3]   Using percentage accuracy to measure neural network predictions in Stock Market movements [J].
Brownstone, D .
NEUROCOMPUTING, 1996, 10 (03) :237-250
[4]   Investment using technical analysis and fuzzy logic [J].
Dourra, H ;
Siy, P .
FUZZY SETS AND SYSTEMS, 2002, 127 (02) :221-240
[5]  
EDWARDS RJ, 1996, TECHNICAL ANAL STOCK, P86
[6]  
Fama E., 1970, EFFICIENT CAPITAL MA
[7]  
Gencay R, 1998, J EMPIR FINANC, V5, P347, DOI [DOI 10.1016/S0927-5398(97)00022-4, 10.1016/j.ribaf.2004.12.009]
[8]   TESTING FOR LINEAR AND NONLINEAR GRANGER CAUSALITY IN THE STOCK PRICE-VOLUME RELATION [J].
HIEMSTRA, C ;
JONES, JD .
JOURNAL OF FINANCE, 1994, 49 (05) :1639-1664
[9]  
Kamijo K., 1990, IJCNN International Joint Conference on Neural Networks (Cat. No.90CH2879-5), P215, DOI 10.1109/IJCNN.1990.137572
[10]   Genetic algorithms approach to feature discretization in artificial neural networks for the prediction of stock price index [J].
Kim, KJ ;
Han, I .
EXPERT SYSTEMS WITH APPLICATIONS, 2000, 19 (02) :125-132