A time-varying study of Chinese investor sentiment, stock market liquidity and volatility: Based on deep learning BERT model and TVP-VAR model

TL;DR

This study uses deep learning and TVP-VAR models to analyze how Chinese investor sentiment influences stock market liquidity and volatility over time, revealing stronger and asymmetric effects during market downturns.

Contribution

It introduces a novel combination of BERT-based sentiment analysis with TVP-VAR modeling to explore dynamic market linkages in China.

Findings

Investor sentiment significantly affects liquidity and volatility.

Impact is stronger during market downturns.

Short-term effects are more pronounced than long-term effects.

Abstract

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.