A novel methodology for stock investment using high utility episode mining and genetic algorithm

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. (C) 2017 Elsevier B.V. All rights reserved.