Finding kernel function for stock market prediction with support vector regression

Stock market prediction is one of the fascinating issues of stock market research. Accurate stock prediction becomes the biggest challenge in investment industry because the distribution of stock data is changing over the time. Time series forcasting, Neural Network (NN) and Support Vector Machine (...

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Main Author:	Chai, Chon Lung
Format:	Thesis
Language:	English
Published:	2006
Subjects:	QA75 Electronic computers Computer science
Online Access:	http://eprints.utm.my/id/eprint/3974/1/ChaiChonLungMFSKSM2006.pdf
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id	my-utm-ep.3974
record_format	uketd_dc
spelling	my-utm-ep.39742018-01-11T04:46:34Z Finding kernel function for stock market prediction with support vector regression 2006-04 Chai, Chon Lung QA75 Electronic computers. Computer science Stock market prediction is one of the fascinating issues of stock market research. Accurate stock prediction becomes the biggest challenge in investment industry because the distribution of stock data is changing over the time. Time series forcasting, Neural Network (NN) and Support Vector Machine (SVM) are once commonly used for prediction on stock price. In this study, the data mining operation called time series forecasting is implemented. The large amount of stock data collected from Kuala Lumpur Stock Exchange is used for the experiment to test the validity of SVMs regression. SVM is a new machine learning technique with principle of structural minimization risk, which have greater generalization ability and proved success in time series prediction. Two kernel functions namely Radial Basis Function and polynomial are compared for finding the accurate prediction values. Besides that, backpropagation neural network are also used to compare the predictions performance. Several experiments are conducted and some analyses on the experimental results are done. The results show that SVM with polynomial kernels provide a promising alternative tool in KLSE stock market prediction. 2006-04 Thesis http://eprints.utm.my/id/eprint/3974/ http://eprints.utm.my/id/eprint/3974/1/ChaiChonLungMFSKSM2006.pdf application/pdf en public masters Universiti Teknologi Malaysia, Faculty of Computer Science and Information System Faculty of Computer Science and Information System
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	QA75 Electronic computers Computer science
spellingShingle	QA75 Electronic computers Computer science Chai, Chon Lung Finding kernel function for stock market prediction with support vector regression
description	Stock market prediction is one of the fascinating issues of stock market research. Accurate stock prediction becomes the biggest challenge in investment industry because the distribution of stock data is changing over the time. Time series forcasting, Neural Network (NN) and Support Vector Machine (SVM) are once commonly used for prediction on stock price. In this study, the data mining operation called time series forecasting is implemented. The large amount of stock data collected from Kuala Lumpur Stock Exchange is used for the experiment to test the validity of SVMs regression. SVM is a new machine learning technique with principle of structural minimization risk, which have greater generalization ability and proved success in time series prediction. Two kernel functions namely Radial Basis Function and polynomial are compared for finding the accurate prediction values. Besides that, backpropagation neural network are also used to compare the predictions performance. Several experiments are conducted and some analyses on the experimental results are done. The results show that SVM with polynomial kernels provide a promising alternative tool in KLSE stock market prediction.
format	Thesis
qualification_level	Master's degree
author	Chai, Chon Lung
author_facet	Chai, Chon Lung
author_sort	Chai, Chon Lung
title	Finding kernel function for stock market prediction with support vector regression
title_short	Finding kernel function for stock market prediction with support vector regression
title_full	Finding kernel function for stock market prediction with support vector regression
title_fullStr	Finding kernel function for stock market prediction with support vector regression
title_full_unstemmed	Finding kernel function for stock market prediction with support vector regression
title_sort	finding kernel function for stock market prediction with support vector regression
granting_institution	Universiti Teknologi Malaysia, Faculty of Computer Science and Information System
granting_department	Faculty of Computer Science and Information System
publishDate	2006
url	http://eprints.utm.my/id/eprint/3974/1/ChaiChonLungMFSKSM2006.pdf
_version_	1747814484897955840

Finding kernel function for stock market prediction with support vector regression

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