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    Rights statement: This is the author’s version of a work that was accepted for publication in Expert Systems with Applications. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Expert Systems with Applications, 42, 23, 2015 DOI: 10.1016/j.eswa.2015.08.010

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Wrapper ANFIS-ICA method to do stock market timing and feature selection on the basis of Japanese Candlestick

Research output: Contribution to journalJournal articlepeer-review

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  • Sasan Barak
  • Jalil Heidary Dahooie
  • Tomáš Tichý
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<mark>Journal publication date</mark>15/12/2015
<mark>Journal</mark>Expert Systems with Applications
Issue number23
Volume42
Number of pages15
Pages (from-to)9221-9235
Publication StatusPublished
Early online date12/08/15
<mark>Original language</mark>English

Abstract

Predicting stock prices is an important objective in the financial world. This paper presents a novel forecasting model for stock markets on the basis of the wrapper ANFIS (Adaptive Neural Fuzzy Inference System)-ICA (Imperialist Competitive Algorithm) and technical analysis of Japanese Candlestick. Two approaches of Raw-based and Signal-based are devised to extract the model's input variables with 15 and 24 features, respectively. The correct predictions percentages for periods of 1–6 days with the total number of buy and sell signals are considered as output variables. In proposed model, the ANFIS prediction results are used as a cost function of wrapper model and ICA is used to select the most appropriate features. This novel combination of feature selection not only takes advantage of ICA optimization swiftness, but also the ANFIS prediction accuracy. The emitted buy and sell signals of the model revealed that Signal databases approach gets better results with 87% prediction accuracy and the wrapper features selection obtains 12% improvement in predictive performance regarding to the base study. In addition, since the wrapper-based feature selection models are considerably more time-consuming, our presented wrapper ANFIS-ICA algorithm's results have superiority in time decreasing as well as increasing prediction accuracy as compared to other algorithms such as wrapper Genetic algorithm (GA).

Bibliographic note

This is the author’s version of a work that was accepted for publication in Expert Systems with Applications. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Expert Systems with Applications, 42, 23, 2015 DOI: 10.1016/j.eswa.2015.08.010