Thai Stock Price Prediction from Daily News Contents
16
Issued Date
2022-01-01
Resource Type
ISSN
23673370
eISSN
23673389
Scopus ID
2-s2.0-85112018305
Journal Title
Lecture Notes in Networks and Systems
Volume
191
Start Page
851
End Page
861
Rights Holder(s)
SCOPUS
Bibliographic Citation
Lecture Notes in Networks and Systems Vol.191 (2022) , 851-861
Suggested Citation
Wattanakul P., Phienthrakul T. Thai Stock Price Prediction from Daily News Contents. Lecture Notes in Networks and Systems Vol.191 (2022) , 851-861. 861. doi:10.1007/978-981-16-0739-4_80 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/84409
Title
Thai Stock Price Prediction from Daily News Contents
Author(s)
Author's Affiliation
Other Contributor(s)
Abstract
Stock market is a major place for investment. Many investors would like to accurately predict the stock price in order to get more profit. Stock price prediction by machine learning techniques is widely used according to an uncertainty movement of the stock price. However, this should be better if the related information is combined. Daily news is a source of information that reflects many factors. Both good and bad factors are indicated in the news. In this research, the news is used to forecast the stock price. Text processing is applied to extract word features. Then, word features are analyzed and selected. The selected features are used in prediction process. Three predictive models, random forest (RF), AdaBoost and support vector regression (SVR), are compared. SVR yields the best result with the lowest root mean square error (RMSE). The result shows that on 50 features from Spearman ranking, SVR is able to reduce the root mean-squared error by 8.63%.