Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/660915
Title: Predicting stock using microblog moods
Authors: Danfeng Yan;Guang Zhou;Xuan Zhao;Yuan Tian;Fangchun Yang
subject: stock prediction|sentiment analysis|microblog
Year: 2016
Publisher: IEEE
Abstract: Some research work has showed that public mood and stock market price have some relations in some degree. Although it is difficult to clear the relation, the research about the relation between stock market price and public mood is interested by some scientists. This paper tries to find the relationship between Chinese stock market and Chinese local Microblog. First, C-POMS (Chinese Profile of Mood States) was proposed to analyze sentiment of Microblog feeds. Then Granger causality test confirmed the relation between C-POMS analysis and price series. SVM and Probabilistic Neural Network were used to make prediction, and experiments show that SVM is better to predict stock market movements than Probabilistic Neural Network. Experiments also indicate that adding certain dimension of C-POMS as the input data will improve the prediction accuracy to 66.667%. Two dimensions to input data leads to the highest accuracy of 71.429%, which is about 20% higher than using only history stock data as the input data. This paper also compared the proposed method with the ROSTEA scores, and concluded that only the proposed method brings more accurate predicts.
Description: 
URI: http://localhost/handle/Hannan/158079
http://localhost/handle/Hannan/660915
ISSN: 1673-5447
volume: 13
issue: 8
Appears in Collections:2016

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Title: Predicting stock using microblog moods
Authors: Danfeng Yan;Guang Zhou;Xuan Zhao;Yuan Tian;Fangchun Yang
subject: stock prediction|sentiment analysis|microblog
Year: 2016
Publisher: IEEE
Abstract: Some research work has showed that public mood and stock market price have some relations in some degree. Although it is difficult to clear the relation, the research about the relation between stock market price and public mood is interested by some scientists. This paper tries to find the relationship between Chinese stock market and Chinese local Microblog. First, C-POMS (Chinese Profile of Mood States) was proposed to analyze sentiment of Microblog feeds. Then Granger causality test confirmed the relation between C-POMS analysis and price series. SVM and Probabilistic Neural Network were used to make prediction, and experiments show that SVM is better to predict stock market movements than Probabilistic Neural Network. Experiments also indicate that adding certain dimension of C-POMS as the input data will improve the prediction accuracy to 66.667%. Two dimensions to input data leads to the highest accuracy of 71.429%, which is about 20% higher than using only history stock data as the input data. This paper also compared the proposed method with the ROSTEA scores, and concluded that only the proposed method brings more accurate predicts.
Description: 
URI: http://localhost/handle/Hannan/158079
http://localhost/handle/Hannan/660915
ISSN: 1673-5447
volume: 13
issue: 8
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7563727.pdf1.25 MBAdobe PDFThumbnail
Preview File
Title: Predicting stock using microblog moods
Authors: Danfeng Yan;Guang Zhou;Xuan Zhao;Yuan Tian;Fangchun Yang
subject: stock prediction|sentiment analysis|microblog
Year: 2016
Publisher: IEEE
Abstract: Some research work has showed that public mood and stock market price have some relations in some degree. Although it is difficult to clear the relation, the research about the relation between stock market price and public mood is interested by some scientists. This paper tries to find the relationship between Chinese stock market and Chinese local Microblog. First, C-POMS (Chinese Profile of Mood States) was proposed to analyze sentiment of Microblog feeds. Then Granger causality test confirmed the relation between C-POMS analysis and price series. SVM and Probabilistic Neural Network were used to make prediction, and experiments show that SVM is better to predict stock market movements than Probabilistic Neural Network. Experiments also indicate that adding certain dimension of C-POMS as the input data will improve the prediction accuracy to 66.667%. Two dimensions to input data leads to the highest accuracy of 71.429%, which is about 20% higher than using only history stock data as the input data. This paper also compared the proposed method with the ROSTEA scores, and concluded that only the proposed method brings more accurate predicts.
Description: 
URI: http://localhost/handle/Hannan/158079
http://localhost/handle/Hannan/660915
ISSN: 1673-5447
volume: 13
issue: 8
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7563727.pdf1.25 MBAdobe PDFThumbnail
Preview File