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


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

October 25, 2019
January 12, 2020
May 20, 2020
sentiment index, realized volatility, GRU

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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