TY - JOUR

T1 - Wrapper ANFIS-ICA method to do stock market timing and feature selection on the basis of Japanese Candlestick

AU - Barak, Sasan

AU - Dahooie, Jalil Heidary

AU - Tichý, Tomáš

N1 - 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

PY - 2015/12/15

Y1 - 2015/12/15

N2 - 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).

AB - 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).

U2 - 10.1016/j.eswa.2015.08.010

DO - 10.1016/j.eswa.2015.08.010

M3 - Journal article

VL - 42

SP - 9221

EP - 9235

JO - Expert Systems with Applications

JF - Expert Systems with Applications

SN - 0957-4174

IS - 23

ER -