A time-varying study of Chinese investor sentiment, stock market liquidity and volatility: Based on deep learning BERT model and TVP-VAR model
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Abstract (arXiv)
Based on the commentary data of the Shenzhen Stock Index bar on the EastMoney website from January 1, 2018 to December 31, 2019. This paper extracts the embedded investor sentiment by using a deep learning BERT model and investigates the time-varying linkage between investment sentiment, stock market liquidity and volatility using a TVP-VAR model. The results show that the impact of investor sentiment on stock market liquidity and volatility is stronger. Although the inverse effect is relatively small, it is more pronounced with the state of the stock market. In all cases, the response is more pronounced in the short term than in the medium to long term, and the impact is asymmetric, with shocks stronger when the market is in a downward spiral.
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References · 3
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
- 2017A Dual-Stage Attention-Based Recurrent Neural Network for Time Series Predictioncited by 245
- 2018Leveraging Financial News for Stock Trend Prediction with Attention-Based Recurrent Neural Networkcited by 6
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