Prediction the direction of SET50 index using support vector machines

Prediction the direction of SET50 index using support vector machines

© 2019 by the Mathematical Association of Thailand. All rights reserved. Support vector machine (SVM) is a very specific type of learning algorithms characterized by the capacity control of the decision function, the use of the kernel functions and the sparsity of the solution. In this paper, we inv...

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Main Authors: Chongkolnee Rungruang, Wilawan Srichaikul, Somsak Chanaim, Songsak Sriboonchitta
Format: Journal
Published: 2019
Subjects:
Mathematics
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http://cmuir.cmu.ac.th/jspui/handle/6653943832/65701
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Institution: Chiang Mai University
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spelling th-cmuir.6653943832-657012019-08-05T04:39:47Z Prediction the direction of SET50 index using support vector machines Chongkolnee Rungruang Wilawan Srichaikul Somsak Chanaim Songsak Sriboonchitta Mathematics © 2019 by the Mathematical Association of Thailand. All rights reserved. Support vector machine (SVM) is a very specific type of learning algorithms characterized by the capacity control of the decision function, the use of the kernel functions and the sparsity of the solution. In this paper, we investigate the predictability of stock index movement direction with SVM by forecasting the daily movement direction of SET 50 index over the period 5 April, 2000 to 22 August, 2018. The experiment results show that SVM with autoregressive lag p = 10 and training data equal 37 have accuracy(ACC) 92.56%. 2019-08-05T04:39:47Z 2019-08-05T04:39:47Z 2019-01-01 Journal 16860209 2-s2.0-85068441927 https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85068441927&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/65701
institution Chiang Mai University
building Chiang Mai University Library
country Thailand
collection CMU Intellectual Repository
topic Mathematics
spellingShingle Mathematics
Chongkolnee Rungruang
Wilawan Srichaikul
Somsak Chanaim
Songsak Sriboonchitta
Prediction the direction of SET50 index using support vector machines
description © 2019 by the Mathematical Association of Thailand. All rights reserved. Support vector machine (SVM) is a very specific type of learning algorithms characterized by the capacity control of the decision function, the use of the kernel functions and the sparsity of the solution. In this paper, we investigate the predictability of stock index movement direction with SVM by forecasting the daily movement direction of SET 50 index over the period 5 April, 2000 to 22 August, 2018. The experiment results show that SVM with autoregressive lag p = 10 and training data equal 37 have accuracy(ACC) 92.56%.
format Journal
author Chongkolnee Rungruang
Wilawan Srichaikul
Somsak Chanaim
Songsak Sriboonchitta
author_facet Chongkolnee Rungruang
Wilawan Srichaikul
Somsak Chanaim
Songsak Sriboonchitta
author_sort Chongkolnee Rungruang
title Prediction the direction of SET50 index using support vector machines
title_short Prediction the direction of SET50 index using support vector machines
title_full Prediction the direction of SET50 index using support vector machines
title_fullStr Prediction the direction of SET50 index using support vector machines
title_full_unstemmed Prediction the direction of SET50 index using support vector machines
title_sort prediction the direction of set50 index using support vector machines
publishDate 2019
url https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85068441927&origin=inward
http://cmuir.cmu.ac.th/jspui/handle/6653943832/65701
_version_ 1681426317898153984

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