A novel methodology for stock investment using high utility episode mining and genetic algorithm |
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| Affiliation: | 1. Department of Computer Science and Information Engineering, National Cheng Kung University, Taiwan, ROC;2. Department of Computer Science and Information Engineering, National University of Kaohsiung, Taiwan, ROC;3. Department of Computer Science, National Chiao Tung University, Taiwan, ROC;1. Department of Computer Science and Engineering, University of Ioannina, GR-45110 Ioannina, Greece;2. Department of Agricultural Technology, Technological Education Institute of Epirus, GR-47100 Arta, Greece;1. Graduate Program in Electrical Engineering, Pontifical Catholic University of Minas Gerais, Av. Dom José Gaspar, 500, 30535-610, Belo Horizonte, MG, Brazil;2. ENSTA-Bretagne, Lab-STICC, 2 rue François Verny, 29806 Brest, France;3. Department of Electrical & Computer Engineering, University of Alberta, Edmonton, T6R 2V4 AB, Canada;4. Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah, 21589, Saudi Arabia;5. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland;1. Centre for Process Integration, School of Chemical Engineering and Analytical Science, The University of Manchester, P.O. Box 88, Manchester, M60 1QD, UK;2. Department of Chemistry and Chemical Engineering, Inha University, 253, Yonghyun-Dong, Nam-Gu, Incheon, 402-751, South Korea;1. Shanghai Key Laboratory of Power Station Automation Technology, School of Mechatronics and Automation, Shanghai University, Shanghai 200072, China;2. Software Engineering Institute, East China Normal University, Shanghai 200062, China |
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| Abstract: | 
In this paper, we present a novel methodology for stock investment using the technique of high utility episode mining and genetic algorithms. Our objective is to devise a profitable episode-based investment model to reveal hidden events that are associated with high utility in the stock market. The time series data of stock price and the derived technical indicators, including moving average, moving average convergence and divergence, random index and bias index, are used for the construction of episode events. We then employ the genetic algorithm for the simultaneous optimization on parameters and selection of subsets of models. The empirical results show that our proposed method significantly outperforms the state-of-the-art methods in terms of annualized returns of investment and precision. We also provide a set of Z-tests to statistically validate the effectiveness of our proposed method. Based upon the promising results obtained, we expect this novel methodology can advance the research in data mining for computational finance and provide an alternative to stock investment in practice. |
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| Keywords: | High utility episode mining Genetic algorithm Stock investment Technical indicators |
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