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Data modeling for Kuala Lumpur composite index with ANFIS

Stock market transaction is one of the most popular investments activities. There are many conventional techniques being used and these include technical and fundamental analysis. Recently, AI such as ANN, GA, FL and RS are widely used by the researchers due to their ability to predict the behavior...

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Main Authors:	Mohd. Yunos, Zuriahati, Shamsuddin, Siti Mariyam, Sallehuddin, Roselina
Format:	Book Section
Published:	IEEE 2008
Subjects:	QA75 Electronic computers. Computer science
Online Access:	http://eprints.utm.my/id/eprint/12523/ http://dx.doi.org/10.1109/AMS.2008.56
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id	my.utm.12523
record_format	eprints
spelling	my.utm.125232017-10-02T08:29:53Z http://eprints.utm.my/id/eprint/12523/ Data modeling for Kuala Lumpur composite index with ANFIS Mohd. Yunos, Zuriahati Shamsuddin, Siti Mariyam Sallehuddin, Roselina QA75 Electronic computers. Computer science Stock market transaction is one of the most popular investments activities. There are many conventional techniques being used and these include technical and fundamental analysis. Recently, AI such as ANN, GA, FL and RS are widely used by the researchers due to their ability to predict the behavior of the stock market efficiently. In this research, a comprehensive preprocessing data modeling of stock market is developed to acquire granular information that represents the behavior of the data that is to be fed to the classifier. The pre-process methodology includes splitting, scaling, normalization, feature selection, and follows by the TenFold Cross Validation method as a benchmark for estimating the predictive accuracy and effectiveness of splitting and selecting the input data. Daily data of KLCI is captured and analyzed, and it is found that the movements of the Indices are unstable; hence the forecasting process becomes difficult. A Hybrid Neurofuzzy with ANFIS is suggested to predict the behavior of the Indices. Four technical indicators are chosen to analyze the data. To verify the effectiveness of the ANFIS model, two experimental have been carried out and the results show that ANFIS method is competent in forecasting the KLCI fabulously compared to ANN. IEEE 2008 Book Section PeerReviewed Mohd. Yunos, Zuriahati and Shamsuddin, Siti Mariyam and Sallehuddin, Roselina (2008) Data modeling for Kuala Lumpur composite index with ANFIS. In: Proceedings - 2nd Asia International Conference on Modelling and Simulation, AMS 2008. IEEE, New York, pp. 609-614. ISBN 978-076953136-6 http://dx.doi.org/10.1109/AMS.2008.56 DOI:10.1109/AMS.2008.56
institution	Universiti Teknologi Malaysia
building	UTM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Teknologi Malaysia
content_source	UTM Institutional Repository
url_provider	http://eprints.utm.my/
topic	QA75 Electronic computers. Computer science
spellingShingle	QA75 Electronic computers. Computer science Mohd. Yunos, Zuriahati Shamsuddin, Siti Mariyam Sallehuddin, Roselina Data modeling for Kuala Lumpur composite index with ANFIS
description	Stock market transaction is one of the most popular investments activities. There are many conventional techniques being used and these include technical and fundamental analysis. Recently, AI such as ANN, GA, FL and RS are widely used by the researchers due to their ability to predict the behavior of the stock market efficiently. In this research, a comprehensive preprocessing data modeling of stock market is developed to acquire granular information that represents the behavior of the data that is to be fed to the classifier. The pre-process methodology includes splitting, scaling, normalization, feature selection, and follows by the TenFold Cross Validation method as a benchmark for estimating the predictive accuracy and effectiveness of splitting and selecting the input data. Daily data of KLCI is captured and analyzed, and it is found that the movements of the Indices are unstable; hence the forecasting process becomes difficult. A Hybrid Neurofuzzy with ANFIS is suggested to predict the behavior of the Indices. Four technical indicators are chosen to analyze the data. To verify the effectiveness of the ANFIS model, two experimental have been carried out and the results show that ANFIS method is competent in forecasting the KLCI fabulously compared to ANN.
format	Book Section
author	Mohd. Yunos, Zuriahati Shamsuddin, Siti Mariyam Sallehuddin, Roselina
author_facet	Mohd. Yunos, Zuriahati Shamsuddin, Siti Mariyam Sallehuddin, Roselina
author_sort	Mohd. Yunos, Zuriahati
title	Data modeling for Kuala Lumpur composite index with ANFIS
title_short	Data modeling for Kuala Lumpur composite index with ANFIS
title_full	Data modeling for Kuala Lumpur composite index with ANFIS
title_fullStr	Data modeling for Kuala Lumpur composite index with ANFIS
title_full_unstemmed	Data modeling for Kuala Lumpur composite index with ANFIS
title_sort	data modeling for kuala lumpur composite index with anfis
publisher	IEEE
publishDate	2008
url	http://eprints.utm.my/id/eprint/12523/ http://dx.doi.org/10.1109/AMS.2008.56
_version_	1643645975329243136
score	13.211869

Data modeling for Kuala Lumpur composite index with ANFIS

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