论文标题
一项关于中国投资者情绪，股票市场流动性和波动性的时变研究：基于深度学习的BERT模型和TVP-VAR模型
A time-varying study of Chinese investor sentiment, stock market liquidity and volatility: Based on deep learning BERT model and TVP-VAR model
论文作者
论文摘要
根据2018年1月1日至2019年12月31日在Eastmoney网站上的深圳股票指数栏的评论数据。本文通过使用深度学习BERT模型提取了嵌入式投资者情感，并研究了使用TVP-VAR模型的投资情感，股票市场流动性和动力学之间的时间变化的联系。结果表明，投资者情绪对股票市场流动性和波动性的影响更强。尽管逆效应相对较小，但股票市场状况更为明显。在所有情况下，短期内的响应比在中长期更为明显，而且影响是不对称的，当市场处于向下的螺旋状态时，震动会更强。
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.
