Computer Science ›› 2017, Vol. 44 ›› Issue (10): 193-202.doi: 10.11896/j.issn.1002-137X.2017.10.036

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Stock Price Movements Prediction Based on Multisources

RAO Dong-ning, DENG Fu-dong and JIANG Zhi-hua   

  • Online:2018-12-01 Published:2018-12-01

Abstract: Predicting stock price movement is a hot topic in the financial intelligence field.So far,people have conti-nuously attempted to use various data sources in the stock price prediction,such as fundamental economic features,technical indicators,Internet public opinions,financial announcements,financial news,financial research reports and so on.However,most of the previous studies use only one or two distinct data sources to build prediction models.Few of them take advantage of three or more sources simultaneously.Undoubtedly,if more sources are provided,people can extract richer information content and consider more information levels.But,since the natures of various sources are distinct,and they have different effects on the stock market,it is not easy to converge several sources in predicting stock price.In addition,multisources naturally increase the risk of suffering the curse of dimensionality.Based on the idea of information fusion,this paper attempted to use three distinct sources to predict the stock price movement.The three sources are fundamental economic features,technical indicators and Internet public opinions.Our method firstly collects various source data,then implements the specific data preprocessing to form a unified data set,and finally uses the SVM classi-fier to build prediction models.Experimental results show that the preformance of prediction model based on the three sources is better than those which use a single source,or sources in pairs,when the linear core function for the SVM classifier is chosen and the data in the non-trading days are added.Besides,when collecting data,we found that the number of Internet public opinions rose sharply,although there were no transactions in the non-trading days (for example,weekends or the suspension period).Therefore,we added more text sentiment data showing the public opinions in the non-trading days and found that the prediction accuracy is improved.The study in this paper shows that although it is difficult to integrate multisources in the stock prediction,it is possible to produce a good predictor after the appropriate feature selection and the specific data preprocessing.

Key words: Multisources,Stock price movement prediction,SVM classification

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