A time-varying study of Chinese investor sentiment, stock market liquidity and volatility: Based on deep learning BERT model and TVP-VAR model
This study addresses the problem of understanding dynamic market behaviors for financial analysts and policymakers, but it is incremental as it applies existing deep learning and econometric methods to new data in a specific domain.
The paper tackled the time-varying relationship between Chinese investor sentiment, stock market liquidity, and volatility by extracting sentiment from online commentary using a BERT model and analyzing it with a TVP-VAR model. The results showed that investor sentiment has a stronger impact on liquidity and volatility, with effects more pronounced in the short term and during market downturns.
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.