Based on 5-minute high-frequency trading data, we examine the time-varying causal relationship between herding behavior and multiscale spillovers (return, volatility, skewness, and kurtosis) in the Chinese stock market. We employ the novel time-varying Granger causality test proposed by Shi et al. (2018), which is based on the recursive evolving algorithm developed by Phillips et al. (2015a, 2015b), to identify real-time causal relationships and capture possible changes in the causal direction. Our findings reveal a strong relationship between herding and spillover effects, particularly with odd-moment (return and skewness) spillovers. For most of the study period, a bidirectional causal relationship was found between herding and odd-moment spillovers. These results imply that herding behavior is a key driver of spillover effects, especially return and skewness spillovers, which are primarily transmitted through the information channel. By contrast, volatility and kurtosis spillovers are more strongly driven by real and financial linkages. Furthermore, spillover effects also affect herding behavior, highlighting the intricate feedback loop between investor behavior and risk transmission.

This paper investigates the price overreaction during the initial continuous trading period of the Chinese corn futures market. Using a dynamic modeling algorithm, we identify the overreaction behavior of intraday high-frequency (1 min and 3 min) prices during the first session of daytime trading. The results indicate that the overreaction hypothesis is confirmed for the daytime prices of the Chinese corn futures market. We also find a noticeable reduction in overreaction following the introduction of night trading and this decline appears to diminish over time. Furthermore, this paper conducts an overreaction trading strategy to assess traders’ returns, revealing a slight decline in average return after the introduction of night trading. This study provides valuable insights and recommendations for exchanges and regulators in monitoring overreaction and formulating effective policies to address it.

Salience theory has been proposed as a new stock trading strategy. Therefore, to assess the validity of this proposal, a complex decision trading system was constructed based on salience theory, a variational mode decomposition (VMD) model, a bidirectional gated recurrent unit (BiGRU) model, and high-frequency trading. The system selected 30 Chinese new energy concept stocks, ranked the stocks using salience theory, and selected the top and bottom three stocks for two portfolios. Twelve stages were established, after which the VMD and BiGRU models were applied to the predictions. The final predicted returns for the high ST group A (GA) were 194.06% and for the low ST group B (GB) were 165.88%. This paper validated the powerful utility of salience theory and deep learning to analyze Chinas new energy market. And it explains the issues and questions raised by previous researchers.