CSI300 volatility predicting by internet users’ searching behavior


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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  1. Apergis, N. (2015). The role of FOMC minutes for US asset prices before and after the 2008 crisis: Evidence from GARCH volatility modeling. Quarterly Review of Economics & Finance, 55, 100–107.

    Article  Google Scholar 

  2. Bentes, S. R. (2015). A comparative analysis of the predictive power of implied volatility indices and GARCH forecasted volatility. Physica A Statistical Mechanics & Its Applications, 424, 105–112.

    Article  Google Scholar 

  3. Bouri, E., Azzi, G., & Dyhrberg, A. H. (2016). On the return-volatility relationship in the bitcoin market around the price crash of 2013. Economics – The Open-Access, The Open-Assessment E-Journal, 11, 1–16. https://doi.org/10.2139/ssrn.2869855.

  4. Brown, M. (2000). Knowledge-based analysis of microarray gene expression data by using support vector machines. PNAS, 97(1), 262–267.

    Article  Google Scholar 

  5. Cao, L. (2003). Support vector machines experts for time series forecasting. Neurocomputing, 51(2), 321–339.

    Google Scholar 

  6. Da, Z., Engelberg, J., & Gao, P. (2015). The sum of all FEARS investor sentiment and asset prices. Social Science Electronic Publishing, 28(10), 1–32.

    Google Scholar 

  7. Garman, M. B., & Klass, M. J. (1980). On the estimation of security Price volatilities from historical data. Journal of Business, 53(1), 67–78.

    Article  Google Scholar 

  8. Gunn, S. (2010). Support vector machines for classification and regression. Analyst, 135(2), 230.

    Article  Google Scholar 

  9. Hansen P. R.,Lunde A.,Nason JM The model confidence set. Econometrica, 2011, 79(2):453–497.

    Article  Google Scholar 

  10. Hossain, A., & Nasser, M. (2011). Recurrent Support and relevance vector machines based model with application to forecasting volatility of financial returns. Journal of Intelligent Learning Systems & Applications, 3(4), 230–241.

    Article  Google Scholar 

  11. Huang, Y., & Kou, G. (2014). A kernel entropy manifold learning approach for financial data analysis. Decision Support Systems, 64(8), 31–42.

    Article  Google Scholar 

  12. Ince, H., & Trafalis, T. (2008). Short term forecasting with Support vector machines and application to stock price prediction. International Journal of General Systems, 37(6), 677–687.

    Article  Google Scholar 

  13. Kara, Y., Acar Boyacioglu, M., & Baykan, Ö. K. (2011). Predicting direction of stock price index movement using artificial neural networks and support vector machines. Expert Systems with Applications, 38(5), 5311–5319.

    Article  Google Scholar 

  14. Kristjanpoller, W., Fadic, A., & Minutolo, M. C. (2014). Volatility forecast using hybrid neural network models. Expert Systems with Applications, 41(5), 2437–2442.

    Article  Google Scholar 

  15. Kung, L. M., & Yu, S. W. (2008). Prediction of index futures returns and the analysis of financial spillovers—A comparison between GARCH and the grey theorem. European Journal of Operational Research, 186(3), 1184–1200.

    Article  Google Scholar 

  16. Liang, Y., Niu, D., Ye, M., & Hong, W. C. (2016). Short-term load forecasting based on wavelet transform and least squares Support vector machine optimized by improved cuckoo search. Energies, 9(10), 827.

    Article  Google Scholar 

  17. Lu, X., Que, D., & Cao, G. (2016). Volatility forecast based on the hybrid artificial neural network and GARCH-type models. Procedia Computer Science, 91, 1044–1049.

    Article  Google Scholar 

  18. Ning, C., Xu, D., & Wirjanto, T. S. (2015). Is volatility clustering of asset returns asymmetric? Journal of Banking & Finance, 52, 62–76.

    Article  Google Scholar 

  19. Osuna, E. (1997). Support vector machines: Training and applications. A. I. Memo no. 1602, C. B. C. L. Paper, 144(9):1308–16.

  20. Pellegrino, F., Coupé, C., & Marsico, E. (2011). Across-language perspective on speech information rate. Language, 87(3), 539–558.

    Article  Google Scholar 

  21. Preis, T., Moat, H. S., & Stanley, H. E. (2013). Quantifying trading behavior in financial markets using Google trends. Scientific Reports, 3, 1684–1691.

    Article  Google Scholar 

  22. Rapach, D. E., Strauss, J. K., & Zhou, G. (2013). International stock return predictability: What is the role of the United States? Social Science Electronic Publishing, 68(4), 1633–1662.

    Google Scholar 

  23. Renjie, H., Qilin, C., Guirao, J. L. G., & Wei, G. (2017). Fuzzy chance constrained least squares twin support vector machine for uncertain classification. Journal of Intelligent & Fuzzy Systems, 33(5), 3041–3049.

  24. Sapankevych, N. I., & Sankar, R. (2009). Time series prediction using support vector machines: A survey. Computational Intelligence Magazine IEEE, 4(2), 24–38.

    Article  Google Scholar 

  25. Wang, Y., Pan, Z., & Wu, C. (2018). Volatility spillover from the US to international stock markets: A heterogeneous volatility spillover GARCH model. Journal of Forecasting, 4, 98–107.

    Google Scholar 

  26. Yang, H., Chan, L., & King, I. (2002). Support vector machine regression for volatile stock market prediction. Intelligent Data Engineering and Automated Learning – IDEAL, 2412, 391–396.

    Google Scholar 

  27. Zhang, Y. J., & Zhang, J. L. (2017). Volatility forecasting of crude oil market: A new hybrid method. Journal of Forecasting, 37, 781–789. https://doi.org/10.1002/for.2502.

    Article  Google Scholar 

  28. Zhou Y-L, Han R-J, et al. (2018). Long short-term memory networks for CSI300 volatilitypredictionwith Baidu search volume. Concurrency and Computation: Practice Experience, e4721.

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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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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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  • Volatility predicting
  • Algorithm accuracy
  • Baidu index