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.