计算机科学 ›› 2020, Vol. 47 ›› Issue (5): 79-83.doi: 10.11896/jsjkx.190400145

• 数据库&大数据&数据科学 • 上一篇    下一篇


赵澄, 叶耀威, 姚明海   

  1. 浙江工业大学信息工程学院 杭州310014
  • 收稿日期:2019-04-26 出版日期:2020-05-15 发布日期:2020-05-19
  • 通讯作者: 姚明海(ymh@zjut.edu.cn)
  • 作者简介:zhaoc@zjut.edu.cn
  • 基金资助:

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).

摘要: 股票市场的情绪可以在一定程度上反映投资者的行为并影响其投资决策。市场新闻作为一种非结构性数据,能够体现并引导市场的大环境情绪,与股票价格一同成为至关重要的市场参考数据,能够为投资者的投资决策提供有效帮助。文中提出了一种可以准确、快速地建立针对海量新闻数据的多维情绪特征向量化方法,利用支持向量机(Support Victor Machine,SVM)模型来预测金融新闻对股票市场的影响,并通过bootstrap来减轻过拟合问题。在沪深股指上进行实验的结果表明,相比于传统模型,所提方法能够将预测准确度提高约8%,并在3个月的回测实验中获得了6.52%的超额收益,证明了其有效性。

关键词: 股票市场预测, 金融情感驱动, 新闻, 文本特征, 交易信号, 人工智能

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


  • TP391
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