Using Market Sentiment Analysis and Genetic Algorithm-Based Least Squares Support Vector Regression to Predict Gold Prices
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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Springer
2020-02-01
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| Series: | International Journal of Computational Intelligence Systems |
| Subjects: | |
| Online Access: | https://www.atlantis-press.com/article/125935074/view |
| _version_ | 1811301356500156416 |
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| author | Fong-Ching Yuan Chao-Hui Lee Chaochang Chiu |
| author_facet | Fong-Ching Yuan Chao-Hui Lee Chaochang Chiu |
| author_sort | Fong-Ching Yuan |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-13T07:07:20Z |
| format | Article |
| id | doaj.art-07cbdc72257f42659c8d094e5b938155 |
| institution | Directory Open Access Journal |
| issn | 1875-6883 |
| language | English |
| last_indexed | 2024-04-13T07:07:20Z |
| publishDate | 2020-02-01 |
| publisher | Springer |
| record_format | Article |
| series | International Journal of Computational Intelligence Systems |
| spelling | doaj.art-07cbdc72257f42659c8d094e5b9381552022-12-22T02:56:57ZengSpringerInternational Journal of Computational Intelligence Systems1875-68832020-02-0113110.2991/ijcis.d.200214.002Using Market Sentiment Analysis and Genetic Algorithm-Based Least Squares Support Vector Regression to Predict Gold PricesFong-Ching YuanChao-Hui LeeChaochang ChiuGold 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.https://www.atlantis-press.com/article/125935074/viewGold price predictionText miningOpinion scoreGenetic algorithmsLeast square support vector regression |
| spellingShingle | Fong-Ching Yuan Chao-Hui Lee Chaochang Chiu Using Market Sentiment Analysis and Genetic Algorithm-Based Least Squares Support Vector Regression to Predict Gold Prices International Journal of Computational Intelligence Systems Gold price prediction Text mining Opinion score Genetic algorithms Least square support vector regression |
| title | Using Market Sentiment Analysis and Genetic Algorithm-Based Least Squares Support Vector Regression to Predict Gold Prices |
| title_full | Using Market Sentiment Analysis and Genetic Algorithm-Based Least Squares Support Vector Regression to Predict Gold Prices |
| title_fullStr | Using Market Sentiment Analysis and Genetic Algorithm-Based Least Squares Support Vector Regression to Predict Gold Prices |
| title_full_unstemmed | Using Market Sentiment Analysis and Genetic Algorithm-Based Least Squares Support Vector Regression to Predict Gold Prices |
| title_short | Using Market Sentiment Analysis and Genetic Algorithm-Based Least Squares Support Vector Regression to Predict Gold Prices |
| title_sort | using market sentiment analysis and genetic algorithm based least squares support vector regression to predict gold prices |
| topic | Gold price prediction Text mining Opinion score Genetic algorithms Least square support vector regression |
| url | https://www.atlantis-press.com/article/125935074/view |
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