International Journal of Computational Intelligence Systems

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Volume 13, Issue 1, 2020, Pages 234 - 246

Using Market Sentiment Analysis and Genetic Algorithm-Based Least Squares Support Vector Regression to Predict Gold Prices

Authors
Fong-Ching Yuan*, Chao-Hui Lee, Chaochang Chiu
Department of Information Management, Innovation Center for Big Data and Digital Convergence, Yuan Ze University, 135 Yuan-Tung Road, Chung-Li, Taoyuan, Taiwan, 32003 R.O.C.
*Corresponding author. Email: imyuan@saturn.yzu.edu.tw
Corresponding Author
Fong-Ching Yuan
Received 17 April 2019, Accepted 11 February 2020, Available Online 23 February 2020.
DOI
10.2991/ijcis.d.200214.002How to use a DOI?
Keywords
Gold price prediction; Text mining; Opinion score; Genetic algorithms; Least square support vector regression
Abstract

Gold price prediction has long been a crucial and challenging research topic for gold investors. In conventional models, most scholars have used the historical gold price or economic indicators to forecast gold prices. The gold prices depend mainly on confidence in the current market. To reduce the time delay of economic indicators in this study, the daily online global gold news undergoes a text mining approach. An opinion score is generated by ascertaining the opinion polarity and words in the daily gold news. The opinion score represents the current market trends and used as an input predictor in the forecasting model. Subsequently, the least square support vector regression (LSSVR) that is optimized by the genetic algorithm (GA) is employed to train and predict the future gold price. The mean absolute percentage error (MAPE) is adopted to evaluate the model performance. This study is the first to use the opinion score through text mining as an input predictor to GA-LSSVR in forecasting gold prices. The experiment results demonstrate that the input predictor, opinion score, can improve the predicting ability of GA-LSSVR model in terms of MAPE.

Copyright
© 2020 The Authors. Published by Atlantis Press SARL.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

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<Previous Article In Issue
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Journal
International Journal of Computational Intelligence Systems
Volume-Issue
13 - 1
Pages
234 - 246
Publication Date
2020/02/23
ISSN (Online)
1875-6883
ISSN (Print)
1875-6891
DOI
10.2991/ijcis.d.200214.002How to use a DOI?
Copyright
© 2020 The Authors. Published by Atlantis Press SARL.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

risenwbib
TY  - JOUR
AU  - Fong-Ching Yuan
AU  - Chao-Hui Lee
AU  - Chaochang Chiu
PY  - 2020
DA  - 2020/02/23
TI  - Using Market Sentiment Analysis and Genetic Algorithm-Based Least Squares Support Vector Regression to Predict Gold Prices
JO  - International Journal of Computational Intelligence Systems
SP  - 234
EP  - 246
VL  - 13
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.d.200214.002
DO  - 10.2991/ijcis.d.200214.002
ID  - Yuan2020
ER  -