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

Authors:
Jar-Long Wang;Shu-Hui Chan
Affiliations:
Department of Management Information System, Fortune Institute of Technology, No. 1-10, Nong-Charng Rd, Dah Liau Countyside, Kaohsiung 831, Taiwan, ROC;Institute of management, National Kaohsiung First University of Science and Technology, Kaohsiung, Taiwan, ROC
Venue:
Expert Systems with Applications: An International Journal
Year:
2006

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Abstract

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.