Forecasting Realized Volatility Based on Sentiment Index and GRU Model

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 qua...

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Bibliographic Details
Published inJournal of advanced computational intelligence and intelligent informatics Vol. 24; no. 3; pp. 299 - 306
Main Authors Gu, Wentao, Zheng, Suhao, Wang, Ru, Dong, Cui
Format Journal Article
LanguageEnglish
Published Tokyo Fuji Technology Press Co. Ltd 01.05.2020
Subjects
Forecasting
Mathematical models
News media
Prediction models
Securities markets
Stock exchanges
Volatility
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Summary: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.
ISSN:1343-0130
1883-8014
DOI:10.20965/jaciii.2020.p0299