Wandi Zhao

The effect of investor sentiment on stock volatility is a highly attractive research question in both the academic field and the real financial industry. With the proposal of China's "dual carbon" target, green stocks have gradually become an essential branch of Chinese stock markets. Focusing on 106 stocks from the new energy, environmental protection, and carbon–neutral sectors, we construct two investor sentiment proxies using Internet text and stock trading data, respectively. The Internet sentiment is based on posts from Eastmoney Guba, and the trading sentiment comes from a variety of trading indicators. In addition, we divide the realized volatility into continuous and jump parts, and then investigate the effects of investor sentiment on different types of volatilities. Our empirical findings show that both sentiment indices impose significant positive impacts on realized, continuous, and jump volatilities, where trading sentiment is the main factor. We further explore the mediating effect of information asymmetry, measured by the volume-synchronized probability of informed trading (VPIN), on the path of investor sentiment affecting stock volatility. It is evidenced that investor sentiments are positively correlated with the VPIN, and they can affect volatilities through the VPIN. We then divide the total sample around the coronavirus disease 2019 (COVID-19) pandemic. The empirical results reveal that the market volatility after the COVID-19 pandemic is more susceptible to investor sentiments, especially to Internet sentiment. Our study is of great significance for maintaining the stability of green stock markets and reducing market volatility.

The measurement of liquidity is the basis of research in market microstructure studies. Based on the intraday tick trading data on Chinese stock markets from 2009 to 2016, we run horseraces of monthly estimates of newly and widely employed low-frequency liquidity proxies in the literature against three types of bid-ask spread high-frequency benchmarks. The empirical results reveal that the closing percent quoted spread estimator has the smallest estimation error, and the FHT estimator has the highest correlation. Moreover, these two estimators win the majority of horseraces in terms of estimation error and correlation comparison with the high-frequency benchmarks. Meanwhile, we find that most liquidity estimators based on Roll’s model do not perform well. Because the performance metrics of estimation precision or correlation performance on related liquidity issues differ depending on the type of research, our study offers appropriate liquidity measures for different research purposes.