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.

This paper investigates how noise trading behavior is influenced by limited attention. As the daily price limit rules of the Chinese stock market provide a scenario for the exhibition of salient payoffs, speculators elevate prices to attract noise traders into the market. Utilizing a series of distraction events stemming from mobile games as exogenous shocks to investors’ attention, we find that the gambler-like behavior, termed as “Hitting game” is crowded out. Consistent with our attention mechanism, indicators such as trading volume decline in response to these game shocks.

We have developed two novel salience factors — PMOR and PMOV based on the stock’s salient return and salient trading volume (as proposed by Cosemans and Frehen, 2021, and Sun et al., 2023). Notably, these factors cannot be accounted for by existing factor models in China. When we integrate the salience trading volume factor — PMOV into Liu et al. (2019)’s Chinese three-factor model, the resulting four-factor model outperforms other models including the Chinese four-factor model in explaining 33 significant anomalies in China.