Computer Science

Computer Science ›› 2020, Vol. 47 ›› Issue (5): 79-83.doi: 10.11896/jsjkx.190400145

• Databωe & Big Data & Data Science • Previous Articles     Next Articles

Stock Volatility Forecast Based on Financial Text Emotion

ZHAO Cheng, YE Yao-wei, YAO Ming-hai   

  1. College of Information Engineering,Zhejiang University of Technology,Hangzhou 310014,China
  • Received:2019-04-26 Online:2020-05-15 Published:2020-05-19
  • About author:ZHAO Cheng,born in 1985,Ph.D,seni-or engineer.His main research intere-sts include quantitative financial and artificial intelligence.
    YAO Ming-hai,born in 1963,professor,Ph.D,doctoral tutor.Hismain research interests include pattern recognition and intelligent control,control theory and control engineering,and computer application.
  • Supported by:
    This work was supported by the National Natural Science Foundation of China (61902349).

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Abstract: Emotions in the stock market can reflect investor behavior to a certain extent and influence investors' investment decisions.As a kind of unstructured data,market news can reflect the advantages and disadvantages of the market environment,and become a vital market reference data with stock prices,which can provide effective help for investment decisions effectively.This paper proposes a multidimensional emotional feature vectorization method which can accurately and quickly establish a large amount of news data for massive news data.It uses the support victor machine (SVM) model to predict the impact of financial news on the stock market,and uses bootstrap to mitigate overfitting problems.The results on Shanghai and Shenzhen stock indexes show that compared with the traditional model,the proposed method can improve the prediction accuracy by about 8% and obtain an excess of 6.52% duringthree months,thus proving the effectiveness of the proposed method.

Key words: Artificial intelligence, Financial emotion driven, News, Stock market prediction, Text feature, Trading signal

CLC Number: 

  • TP391
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