標題: A novel methodology for stock investment using high utility episode mining and genetic algorithm
作者: Lin, Yu-Feng
Huang, Chien-Feng
Tseng, Vincent S.
資訊工程學系
Department of Computer Science
關鍵字: High utility episode mining;Genetic algorithm;Stock investment;Technical indicators
公開日期: 1-Oct-2017
摘要: 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. (C) 2017 Elsevier B.V. All rights reserved.
URI: http://dx.doi.org/10.1016/j.asoc.2017.05.032
http://hdl.handle.net/11536/145925
ISSN: 1568-4946
DOI: 10.1016/j.asoc.2017.05.032
期刊: APPLIED SOFT COMPUTING
Volume: 59
起始頁: 303
結束頁: 315
Appears in Collections:Articles