CSI300 volatility predicting by internet users’ searching behavior

Abstract

Financial market volatility prediction has always been a hot topic in the field of financial mathematics. Inspired by the investor attention theory, many studies using internet users’ online searching behavior to forecast the volatility of financial market, In this paper, we apply ISOMAP-FCC-LSTSVM, FCC-LSTSVM, SVM and GARCH model by using 28 keywords which related to macro-economy and household consumption to predict the CSI300 volatility. Through the data experimental, we compare the accuracy of all the algorithms. Meanwhile, we also verify the conclusion of internet users’ search heterogeneity in our past research which was ignored in former studies.

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Acknowledgments

The authors acknowledge the Chongqing Social Science Doctoral Program (2019BS055) and Chongqing Technology and Business University scientific research fund (1955048).

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Correspondence to Qian Li.

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The author(s) declared no potential conflicts of interest with respect to the research, author- ship, and/or publication of this article.

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Ethics Committee approval was obtained from the Institutional Ethics Committee of Sichuan University to the commencement of the study.

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Han, R., Zeng, Z., Li, Q. et al. CSI300 volatility predicting by internet users’ searching behavior. Curr Psychol (2020). https://doi.org/10.1007/s12144-020-00812-2

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Keywords

  • Volatility predicting
  • Algorithm accuracy
  • Baidu index