Improved particle swarm optimization for fuzzy based stock market turning points prediction

Stock prices usually appear as a series of zigzag patterns that move in upward and downward trends. These zigzag patterns are learned as a tool for predicting the stock market turning points. Identification of these zigzag patterns is a challenge because they occur in multi-resolutions and are hidde...

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Main Author:	Phetchanchai, Chawalsak
Format:	Thesis
Language:	English
Published:	2013
Subjects:	HF Commerce
Online Access:	http://eprints.utm.my/id/eprint/33794/5/ChawalsakPhetchanchaiPFSKSM2013.pdf
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spelling	my-utm-ep.337942017-07-24T01:23:31Z Improved particle swarm optimization for fuzzy based stock market turning points prediction 2013-02 Phetchanchai, Chawalsak HF Commerce Stock prices usually appear as a series of zigzag patterns that move in upward and downward trends. These zigzag patterns are learned as a tool for predicting the stock market turning points. Identification of these zigzag patterns is a challenge because they occur in multi-resolutions and are hidden in the stock prices. Furthermore, learning from these zigzag patterns for prediction of stock market turning points involves vagueness or imprecision. To address these problems, this research proposed the swarm-based stock market turning points prediction model which is a combination of a zigzag patterns extraction method, and a mutation- capable particle swarm optimization method. This model also includes the stepwise regression analysis, adaptive neuro-fuzzy classifier, and subtractive clustering method. This study explores the benefits of the zigzag-based multi-ways search tree data structure to manage the zigzag patterns for extracting interesting zigzag patterns. Furthermore, the mutation capable particle swarm optimization method is used to optimize the parameters of subtractive clustering method for finding the optimal number of fuzzy rules of adaptive neuro-fuzzy classifier. Stepwise regression analysis is used to select the important features from the curse of input dimensions. Finally, adaptive neuro-fuzzy classifier is used for learning the historical turning points from the selected input features and the extracted zigzag patterns to predict stock market turning points. The proposed turning points prediction model is tested using stock market datasets which are the historical data of stocks listed as components of S&P500 index of New York Stock Exchange. These data are stock prices that are either moving upward, downward, or sideways. From the findings, the proposed turning points prediction model has the potential to improve the predictive accuracy, and the performance of stock market trading simulation. 2013-02 Thesis http://eprints.utm.my/id/eprint/33794/ http://eprints.utm.my/id/eprint/33794/5/ChawalsakPhetchanchaiPFSKSM2013.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:69907?site_name=Restricted Repository phd doctoral Universiti Teknologi Malaysia, Faculty of Computing Faculty of Computing
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	HF Commerce
spellingShingle	HF Commerce Phetchanchai, Chawalsak Improved particle swarm optimization for fuzzy based stock market turning points prediction
description	Stock prices usually appear as a series of zigzag patterns that move in upward and downward trends. These zigzag patterns are learned as a tool for predicting the stock market turning points. Identification of these zigzag patterns is a challenge because they occur in multi-resolutions and are hidden in the stock prices. Furthermore, learning from these zigzag patterns for prediction of stock market turning points involves vagueness or imprecision. To address these problems, this research proposed the swarm-based stock market turning points prediction model which is a combination of a zigzag patterns extraction method, and a mutation- capable particle swarm optimization method. This model also includes the stepwise regression analysis, adaptive neuro-fuzzy classifier, and subtractive clustering method. This study explores the benefits of the zigzag-based multi-ways search tree data structure to manage the zigzag patterns for extracting interesting zigzag patterns. Furthermore, the mutation capable particle swarm optimization method is used to optimize the parameters of subtractive clustering method for finding the optimal number of fuzzy rules of adaptive neuro-fuzzy classifier. Stepwise regression analysis is used to select the important features from the curse of input dimensions. Finally, adaptive neuro-fuzzy classifier is used for learning the historical turning points from the selected input features and the extracted zigzag patterns to predict stock market turning points. The proposed turning points prediction model is tested using stock market datasets which are the historical data of stocks listed as components of S&P500 index of New York Stock Exchange. These data are stock prices that are either moving upward, downward, or sideways. From the findings, the proposed turning points prediction model has the potential to improve the predictive accuracy, and the performance of stock market trading simulation.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Phetchanchai, Chawalsak
author_facet	Phetchanchai, Chawalsak
author_sort	Phetchanchai, Chawalsak
title	Improved particle swarm optimization for fuzzy based stock market turning points prediction
title_short	Improved particle swarm optimization for fuzzy based stock market turning points prediction
title_full	Improved particle swarm optimization for fuzzy based stock market turning points prediction
title_fullStr	Improved particle swarm optimization for fuzzy based stock market turning points prediction
title_full_unstemmed	Improved particle swarm optimization for fuzzy based stock market turning points prediction
title_sort	improved particle swarm optimization for fuzzy based stock market turning points prediction
granting_institution	Universiti Teknologi Malaysia, Faculty of Computing
granting_department	Faculty of Computing
publishDate	2013
url	http://eprints.utm.my/id/eprint/33794/5/ChawalsakPhetchanchaiPFSKSM2013.pdf
_version_	1747816186766163968

Improved particle swarm optimization for fuzzy based stock market turning points prediction

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