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International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems

IEA/AIE 2017: Advances in Artificial Intelligence: From Theory to Practice pp 192–201Cite as

Stock Volatility Prediction Using Recurrent Neural Networks with Sentiment Analysis

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10350))

Abstract

In this paper, we propose a model to analyze sentiment of online stock forum and use the information to predict the stock volatility in the Chinese market. We have labeled the sentiment of the online financial posts and make the dataset public available for research. By generating a sentimental dictionary based on financial terms, we develop a model to compute the sentimental score of each online post related to a particular stock. Such sentimental information is represented by two sentiment indicators, which are fused to market data for stock volatility prediction by using the Recurrent Neural Networks (RNNs). Empirical study shows that, comparing to using RNN only, the model performs significantly better with sentimental indicators.

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Notes

  1. 1.

    The founder of Derwent Capital Markets and one of early pioneers in the use of social media sentiment analysis to trade financial derivatives.

  2. 2.

    http://www.indexmundi.com/facts/indicators/CM.MKT.LCAP.CD/rankings.

  3. 3.

    http://guba.eastmoney.com/.

  4. 4.

    http://www.bazhuayu.com.

  5. 5.

    http://dsd.future-lab.cn/members/2016/LiuYFProject/data.xlsx.

  6. 6.

    https://github.com/fxsjy/jieba.

  7. 7.

    github link: https://github.com/irfanICMLL/EMM-for-stock-prediction.

  8. 8.

    It can be downloaded from http://www.gw.com.cn.

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Acknowledgement

This work is supported by the National Science Foundation of China Nos. 61401012 and 61305047.

Author information

Authors and Affiliations

  1. Intelligent Computing and Machine Learning Lab, School of ASEE Beihang University, Beijing, 100191, China

    Yifan Liu, Zengchang Qin & Pengyu Li

  2. School of Mechanical Engineering and Automation Beihang University, Beijing, 100191, China

    Pengyu Li

  3. School of Biological Science and Medical Engineering Beihang University, Beijing, 100191, China

    Tao Wan

Authors
  1. Yifan Liu
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  2. Zengchang Qin
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  3. Pengyu Li
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  4. Tao Wan
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Corresponding authors

Correspondence to Zengchang Qin or Tao Wan .

Editor information

Editors and Affiliations

  1. Artois University, Lens, France

    Salem Benferhat

  2. Artois University, Lens, France

    Karim Tabia

  3. Texas State University, San Marcos, Texas, USA

    Moonis Ali

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Liu, Y., Qin, Z., Li, P., Wan, T. (2017). Stock Volatility Prediction Using Recurrent Neural Networks with Sentiment Analysis. In: Benferhat, S., Tabia, K., Ali, M. (eds) Advances in Artificial Intelligence: From Theory to Practice. IEA/AIE 2017. Lecture Notes in Computer Science(), vol 10350. Springer, Cham. https://doi.org/10.1007/978-3-319-60042-0_22

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  • DOI: https://doi.org/10.1007/978-3-319-60042-0_22

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-60041-3

  • Online ISBN: 978-3-319-60042-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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