Mining associative classification rules with stock trading data – A GA-based method |
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| Authors: | Ya-Wen Chang Chien Yen-Liang Chen |
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| Affiliation: | 1. Division of Information Technology, Netaji Subhas Institute of Technology, University of Delhi, New Delhi, India;2. Department of Computer Science & Software Engineering, Monmouth University, NJ, USA;3. Division of Computer Engineering, Netaji Subhas Institute of Technology, University of Delhi, New Delhi, India;1. School of Business, Sun Yat-sen University, No. 135, Xingang Xi Road, Guangzhou 510275, PR China;2. Institute of Business Intelligence and Knowledge Discovery, Guangdong University of Foreign Studies, Sun Yat-sen University, Guangzhou 510006, PR China;3. School of Business Administration, South China University of Technology, Wushan Road, Tianhe District, Guangzhou 510641, PR China;4. School of Management, Guangdong University of Foreign Studies, Higher Education Mega Center, Guangzhou 510006, PR China;5. University of Kansas Medical Center, Kansas City, KS 66160, USA;1. Technology Foresight Group, Department of Management, Science and Technology, Amirkabir University of Technology, P.O. Box 15875-4413, Tehran, Iran,;2. Department of Industrial Engineering, Amirkabir University of Technology, P.O. Box 15875-4413, Tehran, Iran;3. Member of Futures Studies Research Institute |
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| Abstract: | Associative classifiers are a classification system based on associative classification rules. Although associative classification is more accurate than a traditional classification approach, it cannot handle numerical data and its relationships. Therefore, an ongoing research problem is how to build associative classifiers from numerical data. In this work, we focus on stock trading data with many numerical technical indicators, and the classification problem is finding sell and buy signals from the technical indicators. This study proposes a GA-based algorithm used to build an associative classifier that can discover trading rules from these numerical indicators. The experiment results show that the proposed approach is an effective classification technique with high prediction accuracy and is highly competitive when compared with the data distribution method. |
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