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JACIII Vol.24 No.3 pp. 299-306
doi: 10.20965/jaciii.2020.p0299
(2020)

Paper:

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Forecasting Realized Volatility Based on Sentiment Index and GRU Model

Wentao Gu†, Suhao Zheng, Ru Wang, and Cui Dong

School of Statistics and Mathematics, Zhejiang Gongshang University
18 Xuezheng Street, Xiasha Education Park, Hangzhou, Zhejiang 310018, China

†Corresponding author

Received:
October 25, 2019
Accepted:
January 12, 2020
Published:
May 20, 2020
Creative Commons License
Keywords:
sentiment index, realized volatility, GRU
Abstract

Numerous studies have proven that news media sentiment has an impact on stock market volatility, making topics such as how to quantify news media sentiment and use it to predict stock market volatility increasingly relevant. In this paper, a Chinese financial sentiment lexicon was constructed to quantify the emotions in the news media as a sentiment index to be added to the model and establish new prediction models HAR-RV-AI and GRU-AI. To compare the prediction ability of the models, we consider the loss function and model confidence set (MCS) test as the evaluation criterion and employ the rolling window strategy for out-of-sample forecasting. The prediction results of the GRU model are found to be better than the HAR-RV model, and the prediction effect of the model improved after the addition of the news media sentiment index.

Cite this article as:
W. Gu, S. Zheng, R. Wang, and C. Dong, “Forecasting Realized Volatility Based on Sentiment Index and GRU Model,” J. Adv. Comput. Intell. Intell. Inform., Vol.24 No.3, pp. 299-306, 2020.
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References
  1. [1] J. X. You and J. Wu, “The Spiral of Silence: Media Sentiment and Asset Mispricing,” Economic Research, Vol.2012, No.7, pp. 142-153, 2012 (in Chinese).
  2. [2] C.-H. Wu and C.-J. Lin, “The Impact of Media Coverage on Investor Trading Behavior and Stock Returns,” Pacific-Basin Finance J., pp. 151-172, doi: 10.1016/j.pacfin.2017.04.001, 2017.
  3. [3] S. L. Heston and N. R. Sinha, “News versus Sentiment: Predicting Stock Returns from News Stories,” Staff Working Paper: Finance and Economics Discussion Series (FEDS), No.2016-048, pp. 1-35, Board of Governors of the Federal Reserve System, doi: 10.17016/FEDS.2016.048, 2016.
  4. [4] T. Loughran and B. Mcdonald, “When Is a Liability Not a Liability? Textual Analysis, Dictionaries, and 10-Ks,” The J. of Finance, Vol.66, No.1, pp. 35-65, doi: 10.1111/j.1540-6261.2010.01625.x, 2011.
  5. [5] T. G. Andersen and T. Bollerslev, “Answering the Skeptics: Yes, Standard Volatility Models Do Provide Accurate Forecasts,” Int. Economic Review, Vol.39, No.4, pp. 885-905, 1998.
  6. [6] F. M. Bandi, J. R. Russell, and Y. Zhu, “Using High-Frequency Data in Dynamic Portfolio Choice,” Econometric Reviews, Vol.27, No.1-3, pp. 163-198, doi: 10.1080/07474930701870461, 2008.
  7. [7] G. Dionne, P. Duchesne, and M. Pacurar, “Intraday Value at Risk (IVaR) Using Tick-by-Tick Data with Application to the Toronto Stock Exchange,” J. of Empirical Finance, Vol.16, No.5, pp. 777-792, doi: 10.1016/j.jempfin.2009.05.005, 2009.
  8. [8] R. F. Engle, “Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation,” Econometrica, Vol.50, No.4, pp. 987-1007, doi: 10.2307/1912773, 1982.
  9. [9] P. R. Hansen and A. Lunde, “A forecast comparison of volatility models: does anything beat a GARCH(1,1)?,” J. of Applied Econometrics, Vol.20, No.7, pp. 873-889, doi: 10.1002/jae.800, 2005.
  10. [10] F. Corsi, “A Simple Approximate Long-Memory Model of Realized Volatility,” J. of Financial Econometrics, Vol.7, No.2, pp. 174-196, doi: 10.1093/jjfinec/nbp001, 2009.
  11. [11] D. P. Louzis, S. Xanthopoulos-Sisinis, and A. N. Refenes, “Forecasting Stock Index Realized Volatility with an Asymmetric HAR-FIGARCH Model: The Case of S&P 500 and DJI Stock Indices,” SSRN Electronic J., doi: 10.2139/ssrn.1524861, 2010.
  12. [12] W. Antweiler and M. Z. Frank, “Is All that Talk Just Noise? The Information Content of Internet Stock Message Boards,” The J. of Finance, Vol.59, No.3, pp. 1259-1294, 2004.
  13. [13] Y. Tang, “The Empirical Comparison among Jump Tests of Financial Asset Based on High Frequency Data,” Chinese J. of Management Science, Vol.20, No.S1, pp. 290-299, 2012 (in Chinese).
  14. [14] W. Y. Ye and B. Q. Miao, “Estimating of CVaR with Consideration of Realized Volatility and Price Range,” J. of Management Sciences in China, Vol.15, No.8, pp. 60-71, 2012 (in Chinese).
  15. [15] P. R. Hansen and A. Lunde, “Realized Variance and Market Microstructure Noise,” J. of Business & Economic Statistics, Vol.24, No.2, pp. 127-161, doi: 10.1198/073500106000000071, 2006.
  16. [16] W. K. Newey and K. D. West, “Automatic Lag Selection in Covariance Matrix Estimation,” The Review of Economic Studies, Vol.61, No.4, pp. 631-653, doi: 10.2307/2297912, 1994.
  17. [17] P. R. Hansen, A. Lunde, and J. M. Nason, “The Model Confidence Set,” Econometrica, Vol.79, No.2, pp. 453-497, 2011.
  18. [18] S. Laurent, J. V. K. Rombouts, and F. Violante, “On the Forecasting Accuracy of Multivariate GARCH Models,” J. of Applied Econometrics, Vol.27, No.6, pp. 934-955, doi: 10.1002/jae.1248, 2012.

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