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dc.contributor.authorLin, Yu-Fengen_US
dc.contributor.authorHuang, Chien-Fengen_US
dc.contributor.authorTseng, Vincent S.en_US
dc.date.accessioned2018-08-21T05:54:25Z-
dc.date.available2018-08-21T05:54:25Z-
dc.date.issued2017-10-01en_US
dc.identifier.issn1568-4946en_US
dc.identifier.urihttp://dx.doi.org/10.1016/j.asoc.2017.05.032en_US
dc.identifier.urihttp://hdl.handle.net/11536/145925-
dc.description.abstractIn 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. (C) 2017 Elsevier B.V. All rights reserved.en_US
dc.language.isoen_USen_US
dc.subjectHigh utility episode miningen_US
dc.subjectGenetic algorithmen_US
dc.subjectStock investmenten_US
dc.subjectTechnical indicatorsen_US
dc.titleA novel methodology for stock investment using high utility episode mining and genetic algorithmen_US
dc.typeArticleen_US
dc.identifier.doi10.1016/j.asoc.2017.05.032en_US
dc.identifier.journalAPPLIED SOFT COMPUTINGen_US
dc.citation.volume59en_US
dc.citation.spage303en_US
dc.citation.epage315en_US
dc.contributor.department資訊工程學系zh_TW
dc.contributor.departmentDepartment of Computer Scienceen_US
dc.identifier.wosnumberWOS:000407732600023en_US
Appears in Collections:Articles