Alternative prediction methods in the stock exchanges of Thailand

© 2019 IOP Publishing Ltd. All rights reserved. This paper is conducted to substantially do the two alternatives between the traditional statistical methods such as ARMA and HW models, and artificial intelligence (AI) contains KNN and ELM, respectively. To scope the main object of the paper, SET ind...

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書目詳細資料
Main Authors: Kanchana Chokethaworn, Chukiat Chaiboonsri, Satawat Wannapan
格式: Conference Proceeding
出版: 2020
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在線閱讀:https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85074934844&origin=inward
http://cmuir.cmu.ac.th/jspui/handle/6653943832/68087
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機構: Chiang Mai University
實物特徵
總結:© 2019 IOP Publishing Ltd. All rights reserved. This paper is conducted to substantially do the two alternatives between the traditional statistical methods such as ARMA and HW models, and artificial intelligence (AI) contains KNN and ELM, respectively. To scope the main object of the paper, SET indexes are collected as the main financial variable, which are 5,472 daily observed days during 9 September 1997 to 11 June 2018. Technically, the cross-entropy (CE) analysis, MSE and RMSE calculations are computationally employed to clarify the resolution of the two computations. The empirical results state that the AI prediction can be a substitution replacing the traditional estimations, and this can strongly confirm that machine learning (ML) algorithms are continuously interested, and they are recently becoming a powerful tool for modern econometric forecasting.