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Title: Prediction the direction of SET50 index using support vector machines
Authors: Chongkolnee Rungruang
Wilawan Srichaikul
Somsak Chanaim
Songsak Sriboonchitta
Keywords: Mathematics
Issue Date: 1-Jan-2019
Abstract: © 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%.
ISSN: 16860209
Appears in Collections:CMUL: Journal Articles

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