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

Wrapper ANFIS-ICA method to do stock market timing and feature selection on the basis of Japanese Candlestick
Wrapper ANFIS-ICA method to do stock market timing and feature selection on the basis of Japanese Candlestick
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).
Feature selection, Finance, Stock market forecasting, Technical analysis, Wrapper ANFIS-ICA
0957-4174
9221-9235
Barak, Sasan
f82186de-f5b7-4224-9621-a00e7501f2c3
Dahooie, Jalil Heidary
ad4d4c36-a0c4-4e36-9973-1da3915e48e9
Tichý, Tomáš
fc7bb9da-3e5b-4e78-9bb2-53b22528bac0
Barak, Sasan
f82186de-f5b7-4224-9621-a00e7501f2c3
Dahooie, Jalil Heidary
ad4d4c36-a0c4-4e36-9973-1da3915e48e9
Tichý, Tomáš
fc7bb9da-3e5b-4e78-9bb2-53b22528bac0

Barak, Sasan, Dahooie, Jalil Heidary and Tichý, Tomáš (2015) Wrapper ANFIS-ICA method to do stock market timing and feature selection on the basis of Japanese Candlestick. Expert Systems with Applications, 42 (23), 9221-9235. (doi:10.1016/j.eswa.2015.08.010).

Record type: Article

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).

Full text not available from this repository.

More information

Accepted/In Press date: 5 August 2015
e-pub ahead of print date: 12 August 2015
Published date: 15 December 2015
Keywords: Feature selection, Finance, Stock market forecasting, Technical analysis, Wrapper ANFIS-ICA

Identifiers

Local EPrints ID: 434856
URI: http://eprints.soton.ac.uk/id/eprint/434856
ISSN: 0957-4174
PURE UUID: d84cd0ea-4ebd-4ecd-bf48-3bc839c6c745
ORCID for Sasan Barak: ORCID iD orcid.org/0000-0001-7715-9958

Catalogue record

Date deposited: 11 Oct 2019 16:30
Last modified: 10 Dec 2019 01:20

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